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Causal research: definition, examples and how to use it.

16 min read Causal research enables market researchers to predict hypothetical occurrences & outcomes while improving existing strategies. Discover how this research can decrease employee retention & increase customer success for your business.

What is causal research?

Causal research, also known as explanatory research or causal-comparative research, identifies the extent and nature of cause-and-effect relationships between two or more variables.

It’s often used by companies to determine the impact of changes in products, features, or services process on critical company metrics. Some examples:

• How does rebranding of a product influence intent to purchase?
• How would expansion to a new market segment affect projected sales?
• What would be the impact of a price increase or decrease on customer loyalty?

To maintain the accuracy of causal research, ‘confounding variables’ or influences — e.g. those that could distort the results — are controlled. This is done either by keeping them constant in the creation of data, or by using statistical methods. These variables are identified before the start of the research experiment.

As well as the above, research teams will outline several other variables and principles in causal research:

• Independent variables

The variables that may cause direct changes in another variable. For example, the effect of truancy on a student’s grade point average. The independent variable is therefore class attendance.

• Control variables

These are the components that remain unchanged during the experiment so researchers can better understand what conditions create a cause-and-effect relationship.  

This describes the cause-and-effect relationship. When researchers find causation (or the cause), they’ve conducted all the processes necessary to prove it exists.

• Correlation

Any relationship between two variables in the experiment. It’s important to note that correlation doesn’t automatically mean causation. Researchers will typically establish correlation before proving cause-and-effect.

• Experimental design

Researchers use experimental design to define the parameters of the experiment — e.g. categorizing participants into different groups.

• Dependent variables

These are measurable variables that may change or are influenced by the independent variable. For example, in an experiment about whether or not terrain influences running speed, your dependent variable is the terrain.  

Why is causal research useful?

It’s useful because it enables market researchers to predict hypothetical occurrences and outcomes while improving existing strategies. This allows businesses to create plans that benefit the company. It’s also a great research method because researchers can immediately see how variables affect each other and under what circumstances.

Also, once the first experiment has been completed, researchers can use the learnings from the analysis to repeat the experiment or apply the findings to other scenarios. Because of this, it’s widely used to help understand the impact of changes in internal or commercial strategy to the business bottom line.

Some examples include:

• Understanding how overall training levels are improved by introducing new courses
• Examining which variations in wording make potential customers more interested in buying a product
• Testing a market’s response to a brand-new line of products and/or services

So, how does causal research compare and differ from other research types?

Well, there are a few research types that are used to find answers to some of the examples above:

1. Exploratory research

As its name suggests, exploratory research involves assessing a situation (or situations) where the problem isn’t clear. Through this approach, researchers can test different avenues and ideas to establish facts and gain a better understanding.

Researchers can also use it to first navigate a topic and identify which variables are important. Because no area is off-limits, the research is flexible and adapts to the investigations as it progresses.

Finally, this approach is unstructured and often involves gathering qualitative data, giving the researcher freedom to progress the research according to their thoughts and assessment. However, this may make results susceptible to researcher bias and may limit the extent to which a topic is explored.

2. Descriptive research

Descriptive research is all about describing the characteristics of the population, phenomenon or scenario studied. It focuses more on the “what” of the research subject than the “why”.

For example, a clothing brand wants to understand the fashion purchasing trends amongst buyers in California — so they conduct a demographic survey of the region, gather population data and then run descriptive research. The study will help them to uncover purchasing patterns amongst fashion buyers in California, but not necessarily why those patterns exist.

As the research happens in a natural setting, variables can cross-contaminate other variables, making it harder to isolate cause and effect relationships. Therefore, further research will be required if more causal information is needed.

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How is causal research different from the other two methods above?

Well, causal research looks at what variables are involved in a problem and ‘why’ they act a certain way. As the experiment takes place in a controlled setting (thanks to controlled variables) it’s easier to identify cause-and-effect amongst variables.

Furthermore, researchers can carry out causal research at any stage in the process, though it’s usually carried out in the later stages once more is known about a particular topic or situation.

Finally, compared to the other two methods, causal research is more structured, and researchers can combine it with exploratory and descriptive research to assist with research goals.

Summary of three research types

What are the advantages of causal research?

• Improve experiences

By understanding which variables have positive impacts on target variables (like sales revenue or customer loyalty), businesses can improve their processes, return on investment, and the experiences they offer customers and employees.

• Help companies improve internally

By conducting causal research, management can make informed decisions about improving their employee experience and internal operations. For example, understanding which variables led to an increase in staff turnover.

• Repeat experiments to enhance reliability and accuracy of results

When variables are identified, researchers can replicate cause-and-effect with ease, providing them with reliable data and results to draw insights from.

• Test out new theories or ideas

If causal research is able to pinpoint the exact outcome of mixing together different variables, research teams have the ability to test out ideas in the same way to create viable proof of concepts.

• Fix issues quickly

Once an undesirable effect’s cause is identified, researchers and management can take action to reduce the impact of it or remove it entirely, resulting in better outcomes.

What are the disadvantages of causal research?

• Provides information to competitors

If you plan to publish your research, it provides information about your plans to your competitors. For example, they might use your research outcomes to identify what you are up to and enter the market before you.

• Difficult to administer

Causal research is often difficult to administer because it’s not possible to control the effects of extraneous variables.

• Time and money constraints

Budgetary and time constraints can make this type of research expensive to conduct and repeat. Also, if an initial attempt doesn’t provide a cause and effect relationship, the ROI is wasted and could impact the appetite for future repeat experiments.

• Requires additional research to ensure validity

You can’t rely on just the outcomes of causal research as it’s inaccurate. It’s best to conduct other types of research alongside it to confirm its output.

• Trouble establishing cause and effect

Researchers might identify that two variables are connected, but struggle to determine which is the cause and which variable is the effect.

• Risk of contamination

There’s always the risk that people outside your market or area of study could affect the results of your research. For example, if you’re conducting a retail store study, shoppers outside your ‘test parameters’ shop at your store and skew the results.

How can you use causal research effectively?

To better highlight how you can use causal research across functions or markets, here are a few examples:

Market and advertising research

A company might want to know if their new advertising campaign or marketing campaign is having a positive impact. So, their research team can carry out a causal research project to see which variables cause a positive or negative effect on the campaign.

For example, a cold-weather apparel company in a winter ski-resort town may see an increase in sales generated after a targeted campaign to skiers. To see if one caused the other, the research team could set up a duplicate experiment to see if the same campaign would generate sales from non-skiers. If the results reduce or change, then it’s likely that the campaign had a direct effect on skiers to encourage them to purchase products.

Improving customer experiences and loyalty levels

Customers enjoy shopping with brands that align with their own values, and they’re more likely to buy and present the brand positively to other potential shoppers as a result. So, it’s in your best interest to deliver great experiences and retain your customers.

For example, the Harvard Business Review found that an increase in customer retention rates by 5% increased profits by 25% to 95%. But let’s say you want to increase your own, how can you identify which variables contribute to it?Using causal research, you can test hypotheses about which processes, strategies or changes influence customer retention. For example, is it the streamlined checkout? What about the personalized product suggestions? Or maybe it was a new solution that solved their problem? Causal research will help you find out.

Improving problematic employee turnover rates

If your company has a high attrition rate, causal research can help you narrow down the variables or reasons which have the greatest impact on people leaving. This allows you to prioritize your efforts on tackling the issues in the right order, for the best positive outcomes.

For example, through causal research, you might find that employee dissatisfaction due to a lack of communication and transparency from upper management leads to poor morale, which in turn influences employee retention.

To rectify the problem, you could implement a routine feedback loop or session that enables your people to talk to your company’s C-level executives so that they feel heard and understood.

How to conduct causal research first steps to getting started are:

1. Define the purpose of your research

What questions do you have? What do you expect to come out of your research? Think about which variables you need to test out the theory.

2. Pick a random sampling if participants are needed

Using a technology solution to support your sampling, like a database, can help you define who you want your target audience to be, and how random or representative they should be.

3. Set up the controlled experiment

Once you’ve defined which variables you’d like to measure to see if they interact, think about how best to set up the experiment. This could be in-person or in-house via interviews, or it could be done remotely using online surveys.

4. Carry out the experiment

Make sure to keep all irrelevant variables the same, and only change the causal variable (the one that causes the effect) to gather the correct data. Depending on your method, you could be collecting qualitative or quantitative data, so make sure you note your findings across each regularly.

5. Analyze your findings

Either manually or using technology, analyze your data to see if any trends, patterns or correlations emerge. By looking at the data, you’ll be able to see what changes you might need to do next time, or if there are questions that require further research.

6. Verify your findings

Your first attempt gives you the baseline figures to compare the new results to. You can then run another experiment to verify your findings.

7. Do follow-up or supplemental research

You can supplement your original findings by carrying out research that goes deeper into causes or explores the topic in more detail. One of the best ways to do this is to use a survey. See ‘Use surveys to help your experiment’.

Identifying causal relationships between variables

To verify if a causal relationship exists, you have to satisfy the following criteria:

• Nonspurious association

A clear correlation exists between one cause and the effect. In other words, no ‘third’ that relates to both (cause and effect) should exist.

• Temporal sequence

The cause occurs before the effect. For example, increased ad spend on product marketing would contribute to higher product sales.

• Concomitant variation

The variation between the two variables is systematic. For example, if a company doesn’t change its IT policies and technology stack, then changes in employee productivity were not caused by IT policies or technology.

How surveys help your causal research experiments?

There are some surveys that are perfect for assisting researchers with understanding cause and effect. These include:

• Employee Satisfaction Survey – An introductory employee satisfaction survey that provides you with an overview of your current employee experience.
• Manager Feedback Survey – An introductory manager feedback survey geared toward improving your skills as a leader with valuable feedback from your team.
• Net Promoter Score (NPS) Survey – Measure customer loyalty and understand how your customers feel about your product or service using one of the world’s best-recognized metrics.
• Employee Engagement Survey – An entry-level employee engagement survey that provides you with an overview of your current employee experience.
• Customer Satisfaction Survey – Evaluate how satisfied your customers are with your company, including the products and services you provide and how they are treated when they buy from you.
• Employee Exit Interview Survey – Understand why your employees are leaving and how they’ll speak about your company once they’re gone.
• Product Research Survey – Evaluate your consumers’ reaction to a new product or product feature across every stage of the product development journey.
• Brand Awareness Survey – Track the level of brand awareness in your target market, including current and potential future customers.
• Online Purchase Feedback Survey – Find out how well your online shopping experience performs against customer needs and expectations.

That covers the fundamentals of causal research and should give you a foundation for ongoing studies to assess opportunities, problems, and risks across your market, product, customer, and employee segments.

If you want to transform your research, empower your teams and get insights on tap to get ahead of the competition, maybe it’s time to leverage Qualtrics CoreXM.

Qualtrics CoreXM provides a single platform for data collection and analysis across every part of your business — from customer feedback to product concept testing. What’s more, you can integrate it with your existing tools and services thanks to a flexible API.

Qualtrics CoreXM offers you as much or as little power and complexity as you need, so whether you’re running simple surveys or more advanced forms of research, it can deliver every time.

Get started on your market research journey with CoreXM

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Causal Research: The Complete Guide

Updated: October 07, 2024

Published: January 17, 2023

As we grow up, all humans learn about cause and effect. While it’s not quite as nuanced as causal research, the concept is something our brains begin to comprehend as young as 18 months old. That understanding continues to develop throughout our lives.

In the marketing world, data collection and market research are invaluable. That’s where causal research, the study of cause and effect, comes in.

First-party data can help you learn more about the impact of your marketing campaigns, improve business metrics like customer loyalty, and conduct research on employee productivity. In this guide, we’ll review what causal research is, how it can improve your marketing efforts, and how to conduct your research.

Table of Contents

What is causal research?

The benefits of causal research, causal research examples, how to conduct causal research.

Causal research is a type of study that evaluates whether two variables (one independent, one dependent) have a cause-and-effect relationship. Experiments are designed to collect statistical evidence that infers there is cause and effect between two situations. Marketers can use causal research to see the effect of product changes, rebranding efforts, and more.

Once your team has conducted causal research, your marketers will develop theories on why the relationship developed. Here, your team can study how the variables interact and determine what strategies to apply to future business needs.

Companies can learn how rebranding a product influences sales, how expansion into new markets will affect revenue, and the impact of pricing changes on customer loyalty. Keep in mind that causality is only probable, rather than proven.

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Causal Research: Definition, Design, Tips, Examples

Appinio Research · 21.02.2024 · 33min read

Ever wondered why certain events lead to specific outcomes? Understanding causality—the relationship between cause and effect—is crucial for unraveling the mysteries of the world around us. In this guide on causal research, we delve into the methods, techniques, and principles behind identifying and establishing cause-and-effect relationships between variables. Whether you're a seasoned researcher or new to the field, this guide will equip you with the knowledge and tools to conduct rigorous causal research and draw meaningful conclusions that can inform decision-making and drive positive change.

What is Causal Research?

Causal research is a methodological approach used in scientific inquiry to investigate cause-and-effect relationships between variables. Unlike correlational or descriptive research, which merely examine associations or describe phenomena, causal research aims to determine whether changes in one variable cause changes in another variable.

Importance of Causal Research

Understanding the importance of causal research is crucial for appreciating its role in advancing knowledge and informing decision-making across various fields. Here are key reasons why causal research is significant:

• Establishing Causality:  Causal research enables researchers to determine whether changes in one variable directly cause changes in another variable. This helps identify effective interventions, predict outcomes, and inform evidence-based practices.
• Guiding Policy and Practice:  By identifying causal relationships, causal research provides empirical evidence to support policy decisions, program interventions, and business strategies. Decision-makers can use causal findings to allocate resources effectively and address societal challenges.
• Informing Predictive Modeling :  Causal research contributes to the development of predictive models by elucidating causal mechanisms underlying observed phenomena. Predictive models based on causal relationships can accurately forecast future outcomes and trends.
• Advancing Scientific Knowledge:  Causal research contributes to the cumulative body of scientific knowledge by testing hypotheses, refining theories, and uncovering underlying mechanisms of phenomena. It fosters a deeper understanding of complex systems and phenomena.
• Mitigating Confounding Factors:  Understanding causal relationships allows researchers to control for confounding variables and reduce bias in their studies. By isolating the effects of specific variables, researchers can draw more valid and reliable conclusions.

Causal Research Distinction from Other Research

Understanding the distinctions between causal research and other types of research methodologies is essential for researchers to choose the most appropriate approach for their study objectives. Let's explore the differences and similarities between causal research and descriptive, exploratory, and correlational research methodologies .

Descriptive vs. Causal Research

Descriptive research  focuses on describing characteristics, behaviors, or phenomena without manipulating variables or establishing causal relationships. It provides a snapshot of the current state of affairs but does not attempt to explain why certain phenomena occur.

Causal research , on the other hand, seeks to identify cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. Unlike descriptive research, causal research aims to determine whether changes in one variable directly cause changes in another variable.

Similarities:

• Both descriptive and causal research involve empirical observation and data collection.
• Both types of research contribute to the scientific understanding of phenomena, albeit through different approaches.

Differences:

• Descriptive research focuses on describing phenomena, while causal research aims to explain why phenomena occur by identifying causal relationships.
• Descriptive research typically uses observational methods, while causal research often involves experimental designs or causal inference techniques to establish causality.

Exploratory vs. Causal Research

Exploratory research  aims to explore new topics, generate hypotheses, or gain initial insights into phenomena. It is often conducted when little is known about a subject and seeks to generate ideas for further investigation.

Causal research , on the other hand, is concerned with testing hypotheses and establishing cause-and-effect relationships between variables. It builds on existing knowledge and seeks to confirm or refute causal hypotheses through systematic investigation.

• Both exploratory and causal research contribute to the generation of knowledge and theory development.
• Both types of research involve systematic inquiry and data analysis to answer research questions.
• Exploratory research focuses on generating hypotheses and exploring new areas of inquiry, while causal research aims to test hypotheses and establish causal relationships.
• Exploratory research is more flexible and open-ended, while causal research follows a more structured and hypothesis-driven approach.

Correlational vs. Causal Research

Correlational research  examines the relationship between variables without implying causation. It identifies patterns of association or co-occurrence between variables but does not establish the direction or causality of the relationship.

Causal research , on the other hand, seeks to establish cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. It goes beyond mere association to determine whether changes in one variable directly cause changes in another variable.

• Both correlational and causal research involve analyzing relationships between variables.
• Both types of research contribute to understanding the nature of associations between variables.
• Correlational research focuses on identifying patterns of association, while causal research aims to establish causal relationships.
• Correlational research does not manipulate variables, while causal research involves systematically manipulating independent variables to observe their effects on dependent variables.

How to Formulate Causal Research Hypotheses?

Crafting research questions and hypotheses is the foundational step in any research endeavor. Defining your variables clearly and articulating the causal relationship you aim to investigate is essential. Let's explore this process further.

1. Identify Variables

Identifying variables involves recognizing the key factors you will manipulate or measure in your study. These variables can be classified into independent, dependent, and confounding variables.

• Independent Variable (IV):  This is the variable you manipulate or control in your study. It is the presumed cause that you want to test.
• Dependent Variable (DV):  The dependent variable is the outcome or response you measure. It is affected by changes in the independent variable.
• Confounding Variables:  These are extraneous factors that may influence the relationship between the independent and dependent variables, leading to spurious correlations or erroneous causal inferences. Identifying and controlling for confounding variables is crucial for establishing valid causal relationships.

2. Establish Causality

Establishing causality requires meeting specific criteria outlined by scientific methodology. While correlation between variables may suggest a relationship, it does not imply causation. To establish causality, researchers must demonstrate the following:

• Temporal Precedence:  The cause must precede the effect in time. In other words, changes in the independent variable must occur before changes in the dependent variable.
• Covariation of Cause and Effect:  Changes in the independent variable should be accompanied by corresponding changes in the dependent variable. This demonstrates a consistent pattern of association between the two variables.
• Elimination of Alternative Explanations:  Researchers must rule out other possible explanations for the observed relationship between variables. This involves controlling for confounding variables and conducting rigorous experimental designs to isolate the effects of the independent variable.

3. Write Clear and Testable Hypotheses

Hypotheses serve as tentative explanations for the relationship between variables and provide a framework for empirical testing. A well-formulated hypothesis should be:

• Specific:  Clearly state the expected relationship between the independent and dependent variables.
• Testable:  The hypothesis should be capable of being empirically tested through observation or experimentation.
• Falsifiable:  There should be a possibility of proving the hypothesis false through empirical evidence.

For example, a hypothesis in a study examining the effect of exercise on weight loss could be: "Increasing levels of physical activity (IV) will lead to greater weight loss (DV) among participants (compared to those with lower levels of physical activity)."

By formulating clear hypotheses and operationalizing variables, researchers can systematically investigate causal relationships and contribute to the advancement of scientific knowledge.

Causal Research Design

Designing your research study involves making critical decisions about how you will collect and analyze data to investigate causal relationships.

Experimental vs. Observational Designs

One of the first decisions you'll make when designing a study is whether to employ an experimental or observational design. Each approach has its strengths and limitations, and the choice depends on factors such as the research question, feasibility , and ethical considerations.

• Experimental Design: In experimental designs, researchers manipulate the independent variable and observe its effects on the dependent variable while controlling for confounding variables. Random assignment to experimental conditions allows for causal inferences to be drawn. Example: A study testing the effectiveness of a new teaching method on student performance by randomly assigning students to either the experimental group (receiving the new teaching method) or the control group (receiving the traditional method).
• Observational Design: Observational designs involve observing and measuring variables without intervention. Researchers may still examine relationships between variables but cannot establish causality as definitively as in experimental designs. Example: A study observing the association between socioeconomic status and health outcomes by collecting data on income, education level, and health indicators from a sample of participants.

Control and Randomization

Control and randomization are crucial aspects of experimental design that help ensure the validity of causal inferences.

• Control: Controlling for extraneous variables involves holding constant factors that could influence the dependent variable, except for the independent variable under investigation. This helps isolate the effects of the independent variable. Example: In a medication trial, controlling for factors such as age, gender, and pre-existing health conditions ensures that any observed differences in outcomes can be attributed to the medication rather than other variables.
• Randomization: Random assignment of participants to experimental conditions helps distribute potential confounders evenly across groups, reducing the likelihood of systematic biases and allowing for causal conclusions. Example: Randomly assigning patients to treatment and control groups in a clinical trial ensures that both groups are comparable in terms of baseline characteristics, minimizing the influence of extraneous variables on treatment outcomes.

Internal and External Validity

Two key concepts in research design are internal validity and external validity, which relate to the credibility and generalizability of study findings, respectively.

• Internal Validity: Internal validity refers to the extent to which the observed effects can be attributed to the manipulation of the independent variable rather than confounding factors. Experimental designs typically have higher internal validity due to their control over extraneous variables. Example: A study examining the impact of a training program on employee productivity would have high internal validity if it could confidently attribute changes in productivity to the training intervention.
• External Validity: External validity concerns the extent to which study findings can be generalized to other populations, settings, or contexts. While experimental designs prioritize internal validity, they may sacrifice external validity by using highly controlled conditions that do not reflect real-world scenarios. Example: Findings from a laboratory study on memory retention may have limited external validity if the experimental tasks and conditions differ significantly from real-life learning environments.

Types of Experimental Designs

Several types of experimental designs are commonly used in causal research, each with its own strengths and applications.

• Randomized Control Trials (RCTs): RCTs are considered the gold standard for assessing causality in research. Participants are randomly assigned to experimental and control groups, allowing researchers to make causal inferences. Example: A pharmaceutical company testing a new drug's efficacy would use an RCT to compare outcomes between participants receiving the drug and those receiving a placebo.
• Quasi-Experimental Designs: Quasi-experimental designs lack random assignment but still attempt to establish causality by controlling for confounding variables through design or statistical analysis . Example: A study evaluating the effectiveness of a smoking cessation program might compare outcomes between participants who voluntarily enroll in the program and a matched control group of non-enrollees.

By carefully selecting an appropriate research design and addressing considerations such as control, randomization, and validity, researchers can conduct studies that yield credible evidence of causal relationships and contribute valuable insights to their field of inquiry.

Causal Research Data Collection

Collecting data is a critical step in any research study, and the quality of the data directly impacts the validity and reliability of your findings.

Choosing Measurement Instruments

Selecting appropriate measurement instruments is essential for accurately capturing the variables of interest in your study. The choice of measurement instrument depends on factors such as the nature of the variables, the target population , and the research objectives.

• Surveys :  Surveys are commonly used to collect self-reported data on attitudes, opinions, behaviors, and demographics . They can be administered through various methods, including paper-and-pencil surveys, online surveys, and telephone interviews.
• Observations:  Observational methods involve systematically recording behaviors, events, or phenomena as they occur in natural settings. Observations can be structured (following a predetermined checklist) or unstructured (allowing for flexible data collection).
• Psychological Tests:  Psychological tests are standardized instruments designed to measure specific psychological constructs, such as intelligence, personality traits, or emotional functioning. These tests often have established reliability and validity.
• Physiological Measures:  Physiological measures, such as heart rate, blood pressure, or brain activity, provide objective data on bodily processes. They are commonly used in health-related research but require specialized equipment and expertise.
• Existing Databases:  Researchers may also utilize existing datasets, such as government surveys, public health records, or organizational databases, to answer research questions. Secondary data analysis can be cost-effective and time-saving but may be limited by the availability and quality of data.

Ensuring accurate data collection is the cornerstone of any successful research endeavor. With the right tools in place, you can unlock invaluable insights to drive your causal research forward. From surveys to tests, each instrument offers a unique lens through which to explore your variables of interest.

At Appinio , we understand the importance of robust data collection methods in informing impactful decisions. Let us empower your research journey with our intuitive platform, where you can effortlessly gather real-time consumer insights to fuel your next breakthrough.   Ready to take your research to the next level? Book a demo today and see how Appinio can revolutionize your approach to data collection!

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Sampling Techniques

Sampling involves selecting a subset of individuals or units from a larger population to participate in the study. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population of interest.

• Probability Sampling:  Probability sampling methods involve randomly selecting participants from the population, ensuring that each member of the population has an equal chance of being included in the sample. Common probability sampling techniques include simple random sampling , stratified sampling, and cluster sampling .
• Non-Probability Sampling:  Non-probability sampling methods do not involve random selection and may introduce biases into the sample. Examples of non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling.

The choice of sampling technique depends on factors such as the research objectives, population characteristics, resources available, and practical constraints. Researchers should strive to minimize sampling bias and maximize the representativeness of the sample to enhance the generalizability of their findings.

Ethical Considerations

Ethical considerations are paramount in research and involve ensuring the rights, dignity, and well-being of research participants. Researchers must adhere to ethical principles and guidelines established by professional associations and institutional review boards (IRBs).

• Informed Consent:  Participants should be fully informed about the nature and purpose of the study, potential risks and benefits, their rights as participants, and any confidentiality measures in place. Informed consent should be obtained voluntarily and without coercion.
• Privacy and Confidentiality:  Researchers should take steps to protect the privacy and confidentiality of participants' personal information. This may involve anonymizing data, securing data storage, and limiting access to identifiable information.
• Minimizing Harm:  Researchers should mitigate any potential physical, psychological, or social harm to participants. This may involve conducting risk assessments, providing appropriate support services, and debriefing participants after the study.
• Respect for Participants:  Researchers should respect participants' autonomy, diversity, and cultural values. They should seek to foster a trusting and respectful relationship with participants throughout the research process.
• Publication and Dissemination:  Researchers have a responsibility to accurately report their findings and acknowledge contributions from participants and collaborators. They should adhere to principles of academic integrity and transparency in disseminating research results.

By addressing ethical considerations in research design and conduct, researchers can uphold the integrity of their work, maintain trust with participants and the broader community, and contribute to the responsible advancement of knowledge in their field.

Causal Research Data Analysis

Once data is collected, it must be analyzed to draw meaningful conclusions and assess causal relationships.

Causal Inference Methods

Causal inference methods are statistical techniques used to identify and quantify causal relationships between variables in observational data. While experimental designs provide the most robust evidence for causality, observational studies often require more sophisticated methods to account for confounding factors.

• Difference-in-Differences (DiD):  DiD compares changes in outcomes before and after an intervention between a treatment group and a control group, controlling for pre-existing trends. It estimates the average treatment effect by differencing the changes in outcomes between the two groups over time.
• Instrumental Variables (IV):  IV analysis relies on instrumental variables—variables that affect the treatment variable but not the outcome—to estimate causal effects in the presence of endogeneity. IVs should be correlated with the treatment but uncorrelated with the error term in the outcome equation.
• Regression Discontinuity (RD):  RD designs exploit naturally occurring thresholds or cutoff points to estimate causal effects near the threshold. Participants just above and below the threshold are compared, assuming that they are similar except for their proximity to the threshold.
• Propensity Score Matching (PSM):  PSM matches individuals or units based on their propensity scores—the likelihood of receiving the treatment—creating comparable groups with similar observed characteristics. Matching reduces selection bias and allows for causal inference in observational studies.

Assessing Causality Strength

Assessing the strength of causality involves determining the magnitude and direction of causal effects between variables. While statistical significance indicates whether an observed relationship is unlikely to occur by chance, it does not necessarily imply a strong or meaningful effect.

• Effect Size:  Effect size measures the magnitude of the relationship between variables, providing information about the practical significance of the results. Standard effect size measures include Cohen's d for mean differences and odds ratios for categorical outcomes.
• Confidence Intervals:  Confidence intervals provide a range of values within which the actual effect size is likely to lie with a certain degree of certainty. Narrow confidence intervals indicate greater precision in estimating the true effect size.
• Practical Significance:  Practical significance considers whether the observed effect is meaningful or relevant in real-world terms. Researchers should interpret results in the context of their field and the implications for stakeholders.

Handling Confounding Variables

Confounding variables are extraneous factors that may distort the observed relationship between the independent and dependent variables, leading to spurious or biased conclusions. Addressing confounding variables is essential for establishing valid causal inferences.

• Statistical Control:  Statistical control involves including confounding variables as covariates in regression models to partially out their effects on the outcome variable. Controlling for confounders reduces bias and strengthens the validity of causal inferences.
• Matching:  Matching participants or units based on observed characteristics helps create comparable groups with similar distributions of confounding variables. Matching reduces selection bias and mimics the randomization process in experimental designs.
• Sensitivity Analysis:  Sensitivity analysis assesses the robustness of study findings to changes in model specifications or assumptions. By varying analytical choices and examining their impact on results, researchers can identify potential sources of bias and evaluate the stability of causal estimates.
• Subgroup Analysis:  Subgroup analysis explores whether the relationship between variables differs across subgroups defined by specific characteristics. Identifying effect modifiers helps understand the conditions under which causal effects may vary.

By employing rigorous causal inference methods, assessing the strength of causality, and addressing confounding variables, researchers can confidently draw valid conclusions about causal relationships in their studies, advancing scientific knowledge and informing evidence-based decision-making.

Causal Research Examples

Examples play a crucial role in understanding the application of causal research methods and their impact across various domains. Let's explore some detailed examples to illustrate how causal research is conducted and its real-world implications:

Example 1: Software as a Service (SaaS) User Retention Analysis

Suppose a SaaS company wants to understand the factors influencing user retention and engagement with their platform. The company conducts a longitudinal observational study, collecting data on user interactions, feature usage, and demographic information over several months.

• Design:  The company employs an observational cohort study design, tracking cohorts of users over time to observe changes in retention and engagement metrics. They use analytics tools to collect data on user behavior , such as logins, feature usage, session duration, and customer support interactions.
• Data Collection:  Data is collected from the company's platform logs, customer relationship management (CRM) system, and user surveys. Key metrics include user churn rates, active user counts, feature adoption rates, and Net Promoter Scores ( NPS ).
• Analysis:  Using statistical techniques like survival analysis and regression modeling, the company identifies factors associated with user retention, such as feature usage patterns, onboarding experiences, customer support interactions, and subscription plan types.
• Findings: The analysis reveals that users who engage with specific features early in their lifecycle have higher retention rates, while those who encounter usability issues or lack personalized onboarding experiences are more likely to churn. The company uses these insights to optimize product features, improve onboarding processes, and enhance customer support strategies to increase user retention and satisfaction.

Example 2: Business Impact of Digital Marketing Campaign

Consider a technology startup launching a digital marketing campaign to promote its new product offering. The company conducts an experimental study to evaluate the effectiveness of different marketing channels in driving website traffic, lead generation, and sales conversions.

• Design:  The company implements an A/B testing design, randomly assigning website visitors to different marketing treatment conditions, such as Google Ads, social media ads, email campaigns, or content marketing efforts. They track user interactions and conversion events using web analytics tools and marketing automation platforms.
• Data Collection:  Data is collected on website traffic, click-through rates, conversion rates, lead generation, and sales revenue. The company also gathers demographic information and user feedback through surveys and customer interviews to understand the impact of marketing messages and campaign creatives .
• Analysis:  Utilizing statistical methods like hypothesis testing and multivariate analysis, the company compares key performance metrics across different marketing channels to assess their effectiveness in driving user engagement and conversion outcomes. They calculate return on investment (ROI) metrics to evaluate the cost-effectiveness of each marketing channel.
• Findings:  The analysis reveals that social media ads outperform other marketing channels in generating website traffic and lead conversions, while email campaigns are more effective in nurturing leads and driving sales conversions. Armed with these insights, the company allocates marketing budgets strategically, focusing on channels that yield the highest ROI and adjusting messaging and targeting strategies to optimize campaign performance.

These examples demonstrate the diverse applications of causal research methods in addressing important questions, informing policy decisions, and improving outcomes in various fields. By carefully designing studies, collecting relevant data, employing appropriate analysis techniques, and interpreting findings rigorously, researchers can generate valuable insights into causal relationships and contribute to positive social change.

How to Interpret Causal Research Results?

Interpreting and reporting research findings is a crucial step in the scientific process, ensuring that results are accurately communicated and understood by stakeholders.

Interpreting Statistical Significance

Statistical significance indicates whether the observed results are unlikely to occur by chance alone, but it does not necessarily imply practical or substantive importance. Interpreting statistical significance involves understanding the meaning of p-values and confidence intervals and considering their implications for the research findings.

• P-values:  A p-value represents the probability of obtaining the observed results (or more extreme results) if the null hypothesis is true. A p-value below a predetermined threshold (typically 0.05) suggests that the observed results are statistically significant, indicating that the null hypothesis can be rejected in favor of the alternative hypothesis.
• Confidence Intervals:  Confidence intervals provide a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%). If the confidence interval does not include the null value, it suggests that the observed effect is statistically significant at the specified confidence level.

Interpreting statistical significance requires considering factors such as sample size, effect size, and the practical relevance of the results rather than relying solely on p-values to draw conclusions.

Discussing Practical Significance

While statistical significance indicates whether an effect exists, practical significance evaluates the magnitude and meaningfulness of the effect in real-world terms. Discussing practical significance involves considering the relevance of the results to stakeholders and assessing their impact on decision-making and practice.

• Effect Size:  Effect size measures the magnitude of the observed effect, providing information about its practical importance. Researchers should interpret effect sizes in the context of their field and the scale of measurement (e.g., small, medium, or large effect sizes).
• Contextual Relevance:  Consider the implications of the results for stakeholders, policymakers, and practitioners. Are the observed effects meaningful in the context of existing knowledge, theory, or practical applications? How do the findings contribute to addressing real-world problems or informing decision-making?

Discussing practical significance helps contextualize research findings and guide their interpretation and application in practice, beyond statistical significance alone.

Addressing Limitations and Assumptions

No study is without limitations, and researchers should transparently acknowledge and address potential biases, constraints, and uncertainties in their research design and findings.

• Methodological Limitations:  Identify any limitations in study design, data collection, or analysis that may affect the validity or generalizability of the results. For example, sampling biases , measurement errors, or confounding variables.
• Assumptions:  Discuss any assumptions made in the research process and their implications for the interpretation of results. Assumptions may relate to statistical models, causal inference methods, or theoretical frameworks underlying the study.
• Alternative Explanations:  Consider alternative explanations for the observed results and discuss their potential impact on the validity of causal inferences. How robust are the findings to different interpretations or competing hypotheses?

Addressing limitations and assumptions demonstrates transparency and rigor in the research process, allowing readers to critically evaluate the validity and reliability of the findings.

Communicating Findings Clearly

Effectively communicating research findings is essential for disseminating knowledge, informing decision-making, and fostering collaboration and dialogue within the scientific community.

• Clarity and Accessibility:  Present findings in a clear, concise, and accessible manner, using plain language and avoiding jargon or technical terminology. Organize information logically and use visual aids (e.g., tables, charts, graphs) to enhance understanding.
• Contextualization:  Provide context for the results by summarizing key findings, highlighting their significance, and relating them to existing literature or theoretical frameworks. Discuss the implications of the findings for theory, practice, and future research directions.
• Transparency:  Be transparent about the research process, including data collection procedures, analytical methods, and any limitations or uncertainties associated with the findings. Clearly state any conflicts of interest or funding sources that may influence interpretation.

By communicating findings clearly and transparently, researchers can facilitate knowledge exchange, foster trust and credibility, and contribute to evidence-based decision-making.

Causal Research Tips

When conducting causal research, it's essential to approach your study with careful planning, attention to detail, and methodological rigor. Here are some tips to help you navigate the complexities of causal research effectively:

• Define Clear Research Questions:  Start by clearly defining your research questions and hypotheses. Articulate the causal relationship you aim to investigate and identify the variables involved.
• Consider Alternative Explanations:  Be mindful of potential confounding variables and alternative explanations for the observed relationships. Take steps to control for confounders and address alternative hypotheses in your analysis.
• Prioritize Internal Validity:  While external validity is important for generalizability, prioritize internal validity in your study design to ensure that observed effects can be attributed to the manipulation of the independent variable.
• Use Randomization When Possible:  If feasible, employ randomization in experimental designs to distribute potential confounders evenly across experimental conditions and enhance the validity of causal inferences.
• Be Transparent About Methods:  Provide detailed descriptions of your research methods, including data collection procedures, analytical techniques, and any assumptions or limitations associated with your study.
• Utilize Multiple Methods:  Consider using a combination of experimental and observational methods to triangulate findings and strengthen the validity of causal inferences.
• Be Mindful of Sample Size:  Ensure that your sample size is adequate to detect meaningful effects and minimize the risk of Type I and Type II errors. Conduct power analyses to determine the sample size needed to achieve sufficient statistical power.
• Validate Measurement Instruments:  Validate your measurement instruments to ensure that they are reliable and valid for assessing the variables of interest in your study. Pilot test your instruments if necessary.
• Seek Feedback from Peers:  Collaborate with colleagues or seek feedback from peer reviewers to solicit constructive criticism and improve the quality of your research design and analysis.

Conclusion for Causal Research

Mastering causal research empowers researchers to unlock the secrets of cause and effect, shedding light on the intricate relationships between variables in diverse fields. By employing rigorous methods such as experimental designs, causal inference techniques, and careful data analysis, you can uncover causal mechanisms, predict outcomes, and inform evidence-based practices. Through the lens of causal research, complex phenomena become more understandable, and interventions become more effective in addressing societal challenges and driving progress. In a world where understanding the reasons behind events is paramount, causal research serves as a beacon of clarity and insight. Armed with the knowledge and techniques outlined in this guide, you can navigate the complexities of causality with confidence, advancing scientific knowledge, guiding policy decisions, and ultimately making meaningful contributions to our understanding of the world.

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What is causal research design?

Last updated

14 May 2023

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Examining these relationships gives researchers valuable insights into the mechanisms that drive the phenomena they are investigating.

Organizations primarily use causal research design to identify, determine, and explore the impact of changes within an organization and the market. You can use a causal research design to evaluate the effects of certain changes on existing procedures, norms, and more.

This article explores causal research design, including its elements, advantages, and disadvantages.

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• Components of causal research

You can demonstrate the existence of cause-and-effect relationships between two factors or variables using specific causal information, allowing you to produce more meaningful results and research implications.

These are the key inputs for causal research:

The timeline of events

Ideally, the cause must occur before the effect. You should review the timeline of two or more separate events to determine the independent variables (cause) from the dependent variables (effect) before developing a hypothesis. 

If the cause occurs before the effect, you can link cause and effect and develop a hypothesis .

For instance, an organization may notice a sales increase. Determining the cause would help them reproduce these results. 

Upon review, the business realizes that the sales boost occurred right after an advertising campaign. The business can leverage this time-based data to determine whether the advertising campaign is the independent variable that caused a change in sales. 

Evaluation of confounding variables

In most cases, you need to pinpoint the variables that comprise a cause-and-effect relationship when using a causal research design. This uncovers a more accurate conclusion. 

Co-variations between a cause and effect must be accurate, and a third factor shouldn’t relate to cause and effect. 

Observing changes

Variation links between two variables must be clear. A quantitative change in effect must happen solely due to a quantitative change in the cause. 

You can test whether the independent variable changes the dependent variable to evaluate the validity of a cause-and-effect relationship. A steady change between the two variables must occur to back up your hypothesis of a genuine causal effect. 

• Why is causal research useful?

Causal research allows market researchers to predict hypothetical occurrences and outcomes while enhancing existing strategies. Organizations can use this concept to develop beneficial plans. 

Causal research is also useful as market researchers can immediately deduce the effect of the variables on each other under real-world conditions. 

Once researchers complete their first experiment, they can use their findings. Applying them to alternative scenarios or repeating the experiment to confirm its validity can produce further insights. 

Businesses widely use causal research to identify and comprehend the effect of strategic changes on their profits. 

• How does causal research compare and differ from other research types?

Other research types that identify relationships between variables include exploratory and descriptive research . 

Here’s how they compare and differ from causal research designs:

Exploratory research

An exploratory research design evaluates situations where a problem or opportunity's boundaries are unclear. You can use this research type to test various hypotheses and assumptions to establish facts and understand a situation more clearly.

You can also use exploratory research design to navigate a topic and discover the relevant variables. This research type allows flexibility and adaptability as the experiment progresses, particularly since no area is off-limits.

It’s worth noting that exploratory research is unstructured and typically involves collecting qualitative data . This provides the freedom to tweak and amend the research approach according to your ongoing thoughts and assessments. 

Unfortunately, this exposes the findings to the risk of bias and may limit the extent to which a researcher can explore a topic. 

This table compares the key characteristics of causal and exploratory research:

Descriptive research

This research design involves capturing and describing the traits of a population, situation, or phenomenon. Descriptive research focuses more on the " what " of the research subject and less on the " why ."

Since descriptive research typically happens in a real-world setting, variables can cross-contaminate others. This increases the challenge of isolating cause-and-effect relationships. 

You may require further research if you need more causal links. 

This table compares the key characteristics of causal and descriptive research.  

Causal research examines a research question’s variables and how they interact. It’s easier to pinpoint cause and effect since the experiment often happens in a controlled setting. 

Researchers can conduct causal research at any stage, but they typically use it once they know more about the topic.

In contrast, causal research tends to be more structured and can be combined with exploratory and descriptive research to help you attain your research goals. 

• How can you use causal research effectively?

Here are common ways that market researchers leverage causal research effectively:

Market and advertising research

Do you want to know if your new marketing campaign is affecting your organization positively? You can use causal research to determine the variables causing negative or positive impacts on your campaign. 

Improving customer experiences and loyalty levels

Consumers generally enjoy purchasing from brands aligned with their values. They’re more likely to purchase from such brands and positively represent them to others. 

You can use causal research to identify the variables contributing to increased or reduced customer acquisition and retention rates. 

Could the cause of increased customer retention rates be streamlined checkout? 

Perhaps you introduced a new solution geared towards directly solving their immediate problem. 

Whatever the reason, causal research can help you identify the cause-and-effect relationship. You can use this to enhance your customer experiences and loyalty levels.

Improving problematic employee turnover rates

Is your organization experiencing skyrocketing attrition rates? 

You can leverage the features and benefits of causal research to narrow down the possible explanations or variables with significant effects on employees quitting. 

This way, you can prioritize interventions, focusing on the highest priority causal influences, and begin to tackle high employee turnover rates. 

• Advantages of causal research

The main benefits of causal research include the following:

Effectively test new ideas

If causal research can pinpoint the precise outcome through combinations of different variables, researchers can test ideas in the same manner to form viable proof of concepts.

Achieve more objective results

Market researchers typically use random sampling techniques to choose experiment participants or subjects in causal research. This reduces the possibility of exterior, sample, or demography-based influences, generating more objective results. 

Improved business processes

Causal research helps businesses understand which variables positively impact target variables, such as customer loyalty or sales revenues. This helps them improve their processes, ROI, and customer and employee experiences.

Guarantee reliable and accurate results

Upon identifying the correct variables, researchers can replicate cause and effect effortlessly. This creates reliable data and results to draw insights from. 

Internal organization improvements

Businesses that conduct causal research can make informed decisions about improving their internal operations and enhancing employee experiences. 

• Disadvantages of causal research

Like any other research method, casual research has its set of drawbacks that include:

Extra research to ensure validity

Researchers can't simply rely on the outcomes of causal research since it isn't always accurate. There may be a need to conduct other research types alongside it to ensure accurate output.

Coincidence

Coincidence tends to be the most significant error in causal research. Researchers often misinterpret a coincidental link between a cause and effect as a direct causal link. 

Administration challenges

Causal research can be challenging to administer since it's impossible to control the impact of extraneous variables . 

Giving away your competitive advantage

If you intend to publish your research, it exposes your information to the competition. 

Competitors may use your research outcomes to identify your plans and strategies to enter the market before you. 

• Causal research examples

Multiple fields can use causal research, so it serves different purposes, such as. 

Customer loyalty research

Organizations and employees can use causal research to determine the best customer attraction and retention approaches. 

They monitor interactions between customers and employees to identify cause-and-effect patterns. That could be a product demonstration technique resulting in higher or lower sales from the same customers. 

Example: Business X introduces a new individual marketing strategy for a small customer group and notices a measurable increase in monthly subscriptions. 

Upon getting identical results from different groups, the business concludes that the individual marketing strategy resulted in the intended causal relationship.

Advertising research

Businesses can also use causal research to implement and assess advertising campaigns. 

Example: Business X notices a 7% increase in sales revenue a few months after a business introduces a new advertisement in a certain region. The business can run the same ad in random regions to compare sales data over the same period. 

This will help the company determine whether the ad caused the sales increase. If sales increase in these randomly selected regions, the business could conclude that advertising campaigns and sales share a cause-and-effect relationship. 

Educational research

Academics, teachers, and learners can use causal research to explore the impact of politics on learners and pinpoint learner behavior trends. 

Example: College X notices that more IT students drop out of their program in their second year, which is 8% higher than any other year. 

The college administration can interview a random group of IT students to identify factors leading to this situation, including personal factors and influences. 

With the help of in-depth statistical analysis, the institution's researchers can uncover the main factors causing dropout. They can create immediate solutions to address the problem.

Is a causal variable dependent or independent?

When two variables have a cause-and-effect relationship, the cause is often called the independent variable. As such, the effect variable is dependent, i.e., it depends on the independent causal variable. An independent variable is only causal under experimental conditions. 

What are the three criteria for causality?

The three conditions for causality are:

Temporality/temporal precedence: The cause must precede the effect.

Rationality: One event predicts the other with an explanation, and the effect must vary in proportion to changes in the cause.

Control for extraneous variables: The covariables must not result from other variables.  

Is causal research experimental?

Causal research is mostly explanatory. Causal studies focus on analyzing a situation to explore and explain the patterns of relationships between variables. 

Further, experiments are the primary data collection methods in studies with causal research design. However, as a research design, causal research isn't entirely experimental.

What is the difference between experimental and causal research design?

One of the main differences between causal and experimental research is that in causal research, the research subjects are already in groups since the event has already happened. 

On the other hand, researchers randomly choose subjects in experimental research before manipulating the variables.

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Causal Research: What it is, Tips & Examples

Causal research is classified as conclusive research since it attempts to build a cause-and-effect link between two variables. This research is mainly used to determine the cause of particular behavior. We can use this research to determine what changes occur in an independent variable due to a change in the dependent variable.

It can assist you in evaluating marketing activities, improving internal procedures, and developing more effective business plans. Understanding how one circumstance affects another may help you determine the most effective methods for satisfying your business needs.

LEARN ABOUT: Behavioral Research

This post will explain causal research, define its essential components, describe its benefits and limitations, and provide some important tips.

Content Index

What is causal research?

Temporal sequence, non-spurious association, concomitant variation, the advantages, the disadvantages, causal research examples, causal research tips.

Causal research is also known as explanatory research . It’s a type of research that examines if there’s a cause-and-effect relationship between two separate events. This would occur when there is a change in one of the independent variables, which is causing changes in the dependent variable.

You can use causal research to evaluate the effects of particular changes on existing norms, procedures, and so on. This type of research examines a condition or a research problem to explain the patterns of interactions between variables.

LEARN ABOUT: Research Process Steps

Components of causal research

Only specific causal information can demonstrate the existence of cause-and-effect linkages. The three key components of causal research are as follows:

Prior to the effect, the cause must occur. If the cause occurs before the appearance of the effect, the cause and effect can only be linked. For example, if the profit increase occurred before the advertisement aired, it cannot be linked to an increase in advertising spending.

Linked fluctuations between two variables are only allowed if there is no other variable that is related to both cause and effect. For example, a notebook manufacturer has discovered a correlation between notebooks and the autumn season. They see that during this season, more people buy notebooks because students are buying them for the upcoming semester.

During the summer, the company launched an advertisement campaign for notebooks. To test their assumption, they can look up the campaign data to see if the increase in notebook sales was due to the student’s natural rhythm of buying notebooks or the advertisement.

Concomitant variation is defined as a quantitative change in effect that happens solely as a result of a quantitative change in the cause. This means that there must be a steady change between the two variables. You can examine the validity of a cause-and-effect connection by seeing if the independent variable causes a change in the dependent variable.

For example, if any company does not make an attempt to enhance sales by acquiring skilled employees or offering training to them, then the hire of experienced employees cannot be credited for an increase in sales. Other factors may have contributed to the increase in sales.

Causal Research Advantages and Disadvantages

Causal or explanatory research has various advantages for both academics and businesses. As with any other research method, it has a few disadvantages that researchers should be aware of. Let’s look at some of the advantages and disadvantages of this research design .

• Helps in the identification of the causes of system processes. This allows the researcher to take the required steps to resolve issues or improve outcomes.
• It provides replication if it is required.
• Causal research assists in determining the effects of changing procedures and methods.
• Subjects are chosen in a methodical manner. As a result, it is beneficial for improving internal validity .
• The ability to analyze the effects of changes on existing events, processes, phenomena, and so on.
• Finds the sources of variable correlations, bridging the gap in correlational research .
• It is not always possible to monitor the effects of all external factors, so causal research is challenging to do.
• It is time-consuming and might be costly to execute.
• The effect of a large range of factors and variables existing in a particular setting makes it difficult to draw results.
• The most major error in this research is a coincidence. A coincidence between a cause and an effect can sometimes be interpreted as a direction of causality.
• To corroborate the findings of the explanatory research , you must undertake additional types of research. You can’t just make conclusions based on the findings of a causal study.
• It is sometimes simple for a researcher to see that two variables are related, but it can be difficult for a researcher to determine which variable is the cause and which variable is the effect.

Since different industries and fields can carry out causal comparative research , it can serve many different purposes. Let’s discuss 3 examples of causal research:

Advertising Research

Companies can use causal research to enact and study advertising campaigns. For example, six months after a business debuts a new ad in a region. They see a 5% increase in sales revenue.

To assess whether the ad has caused the lift, they run the same ad in randomly selected regions so they can compare sales data across regions over another six months. When sales pick up again in these regions, they can conclude that the ad and sales have a valuable cause-and-effect relationship.

LEARN ABOUT: Ad Testing

Customer Loyalty Research

Businesses can use causal research to determine the best customer retention strategies. They monitor interactions between associates and customers to identify patterns of cause and effect, such as a product demonstration technique leading to increased or decreased sales from the same customers.

For example, a company implements a new individual marketing strategy for a small group of customers and sees a measurable increase in monthly subscriptions. After receiving identical results from several groups, they concluded that the one-to-one marketing strategy has the causal relationship they intended.

Educational Research

Learning specialists, academics, and teachers use causal research to learn more about how politics affects students and identify possible student behavior trends. For example, a university administration notices that more science students drop out of their program in their third year, which is 7% higher than in any other year.

They interview a random group of science students and discover many factors that could lead to these circumstances, including non-university components. Through the in-depth statistical analysis, researchers uncover the top three factors, and management creates a committee to address them in the future.

Causal research is frequently the last type of research done during the research process and is considered definitive. As a result, it is critical to plan the research with specific parameters and goals in mind. Here are some tips for conducting causal research successfully:

1. Understand the parameters of your research

Identify any design strategies that change the way you understand your data. Determine how you acquired data and whether your conclusions are more applicable in practice in some cases than others.

2. Pick a random sampling strategy

Choosing a technique that works best for you when you have participants or subjects is critical. You can use a database to generate a random list, select random selections from sorted categories, or conduct a survey.

3. Determine all possible relations

Examine the different relationships between your independent and dependent variables to build more sophisticated insights and conclusions.

To summarize, causal or explanatory research helps organizations understand how their current activities and behaviors will impact them in the future. This is incredibly useful in a wide range of business scenarios. This research can ensure the outcome of various marketing activities, campaigns, and collaterals. Using the findings of this research program, you will be able to design more successful business strategies that take advantage of every business opportunity.

At QuestionPro, we offer all kinds of necessary tools for researchers to carry out their projects. It can help you get the most out of your data by guiding you through the process.

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• What is Causal Research? Definition + Key Elements

Cause-and-effect relationships happen in all aspects of life, from business to medicine, to marketing, to education, and so much more. They are the invisible threads that connect both our actions and inactions to their outcomes. 

Causal research is the type of research that investigates cause-and-effect relationships. It is more comprehensive than descriptive research, which just talks about how things affect each other.

Let’s take a closer look at how you can use informal research to gain insight into your research results and make more informed decisions.

Defining Causal Research

Causal research investigates why one variable (the independent variable) is causing things to change in another ( the dependent variable). 

For example, a causal research study about the cause-and-effect relationship between smoking and the prevalence of lung cancer. Smoking prevalence would be the independent variable, while lung cancer prevalence would be the dependent variable. 

You would establish that smoking causes lung cancer by modulating the independent variable (smoking) and observing the effects on the dependent variable (lung cancer).

What’s the Difference Between Correlation and Causation

Correlation simply means that two variables are related to each other. But it does not necessarily mean that one variable causes changes in the other. 

For example, let’s say there is a correlation between high coffee sales and low ice cream sales. This does not mean that people are not buying ice cream because they prefer coffee. 

Both of these variables correlate because they’re influenced by the same factor: cold weather.

The Need for Causal Research

The major reason for investigating causal relationships between variables is better decision-making , which leads to developing effective solutions to complex problems. Here’s a breakdown of how it works:

• Decision-Making

Causal research enables us to figure out how variables relate to each other and how a change in one variable affects another. This helps us make better decisions about resource allocation, problem-solving, and achieving our goals.

In business, for example, customer satisfaction (independent variable) directly impacts sales (dependent variable). If customers are happy with your product or service, they’re more likely to keep returning and recommending it to their friends, which translates into more sales.

• Developing Effective Solutions to Problems

Understanding the causes of a problem,  allows you to develop more effective solutions to address it. For example, medical causal research enables you to understand symptoms better, create new prevention strategies, and provide more effective treatment for illnesses.

Examples of Where Causal Relationships Are Critical

Here are a couple of ways  you can leverage causal research:

• Policy-making : Causal research informs policy decisions about issues such as education, healthcare, and the environment. Let’s say causal research shows that the availability of junk food in schools directly impacts the prevalence of obesity in teenagers. This would inform the decision to incorporate more healthy food options in schools.
• Marketing strategies : Causal research studies allow you to identify factors that influence customer behavior to develop effective marketing strategies. For example, you can use causal research to reach and attract your target audience with the right content.
• Product development : Causal research enables you to create successful products by understanding users’ pain points and providing products that meet these needs.

Key Elements of Causal Research

Let’s take a deep dive into what it takes to design and conduct a causal study:

• Control and Experimental Groups

In a controlled study, the researchers randomly put people into one of two groups: the control group, who don’t get the treatment, or the experimental group, who do.

Having a control group allows you to compare the effects of the treatment to the effects of no treatment. It enables you to rule out the possibility that any changes in the dependent variable are due to factors other than the treatment.

• Independent variable : The independent variable is the variable that affects the dependent variable. It is the variable that you alter to see the effect on the dependent variable.
• Dependent variable : The dependent variable is the variable that is affected by the independent variable. This is what you measure to see the impact of the independent variable.

An Illustration of How Independent vs Dependent Variable Works in Causal Research

Here’s an illustration to help you understand how to differentiate and use variables in causal research:

Let’s say you want to investigate “ the effect of dieting on weight loss ”, dieting would be the independent variable, and weight loss would be the dependent variable. Next, you would vary the independent variable (dieting) by assigning some participants to a restricted diet and others to a control group. 

You will see the cause-and-effect relationship between dieting and weight loss by measuring the dependent variable (weight loss) in both groups.

Skip the setup hassle! Get a head start on your research with our ready-to-use Experimental Research Survey Template

Research Designs for Establishing Causality

There are several ways to investigate the relationship between variables, but here are the most common:

A. Experimental Design

Experimental designs are the gold standard for establishing causality. In an experimental design, the researcher randomly assigns participants to either a control group or an experimental group. The control group does not receive the treatment, while the experimental group does.

Pros of experimental designs :

• Highly rigorous
• Explicitly establishes causality
• Strictly controls for extraneous variables
• Time-consuming and expensive
• Difficult to implement in real-world settings
• Not always ethical

B. Quasi-Experimental Design

A quasi-experimental design attempts to determine the causal relationship without fully randomizing the participant distribution into groups. The primary reason for this is ethical or practical considerations.

Different types of quasi-experimental designs

• Time series design : This design involves collecting data over time on the same group of participants. You see the cause-and-effect relationship by identifying the changes in the dependent variable that coincide with changes in the independent variable.
• Nonequivalent control group design : This design involves comparing an experimental group to a control group that is not randomly assigned. The differences between the two groups explain the cause-and-effect relationship.
• Interrupted time series design : Unlike the time series that measures changes over time, this introduces treatment at a specific point in time. You figure out the relationship between treatment and the dependent variable by looking for any changes that occurred at the time the treatment was introduced.

Pros of quasi-experimental designs

• Cost-effective
• More feasible to implement in real-world settings
• More ethical than experimental designs
• Not as thorough as experimental designs
• May not accurately establish causality
• More susceptible to bias

Establishing Causality without Experiments

Using experiments to determine the cause-and-effect relationship between each dependent variable and the independent variable can be time-consuming and expensive. As a result, the following are cost-effective methods for establishing a causal relationship:

• Longitudinal Studies

Long-term studies are observational studies that follow the same participants or groups over a long period. This way, you can see changes in variables you’re studying over time, and establish a causal relationship between them.

For example, you can use a longitudinal study to determine the effect of a new education program on student performance. You then track students’ academic performance over the years to see if the program improved student performance.

Challenges of Longitudinal Studies

One of the biggest problems of longitudinal studies is confounding variables. These are factors that are related to both the independent variable and the dependent variable.

Confounding variables can make it hard to isolate the cause of an independent variable’s effect. Using the earlier example, if you’re looking at how new educational programs affect student success, you need to make sure you’re controlling for factors such as students’ socio-economic background and their prior academic performance.

• Instrumental Variables (IV) Analysis

Instrumental variable analysis (IV) is a statistical approach that enables you to estimate causal effects in observational studies. An instrumental variable is a variable that is correlated with the independent variable but is not correlated with the dependent variable except through the independent variable.

For example, in academic achievement research, an instrumental variable could be the distance to the nearest college. This variable is correlated with family income but doesn’t correlate with academic achievement except through family income.

Challenges of Instrumental Variables (IV) Analysis

A primary limitation of IV analysis is that it can be challenging to find a good instrumental variable. IV analysis can also be very sensitive to the assumptions of the model.

Challenges and Pitfalls

It is a powerful tool for solving problems, making better decisions, and advancing human knowledge. However, causal research is not without its challenges and pitfalls.

• Confounding Variables

A confounding variable is a variable that correlates with both the independent and dependent variables, and it can make it difficult to isolate the causal effect of the independent variable. 

For example, let’s say you are interested in the causal effect of smoking on lung cancer. If you simply compare smokers to nonsmokers, you may find that smokers are more likely to get lung cancer. 

However, the relationship between smoking and lung cancer may be confounded by other factors, such as age, socioeconomic status, or exposure to secondhand smoke. These other factors may be responsible for the increased risk of lung cancer in smokers, rather than smoking itself.

Unlock the research secrets that top professionals use: Get the facts you need about Desk Research here 

Strategy to Control for Confounding Variables

Confounding variables can lead to misleading results and make it difficult to determine the cause-and-effect between variables. Here are some strategies that allow you to control for confounding variables and improve the reliability of causal research findings:

• Randomized Controlled Trial (RCT)

In an RCT, participants are randomly assigned to either the treatment group or the control group. This ensures that the two groups are comparable on all confounding variables, except for the treatment itself.

• Statistical Methods

Using statistical methods such as multivariate regression analysis allows you to control for multiple confounding variables simultaneously.

Reverse Causation

Reverse Causation is when the relationship between the cause and effect of variables is reversed. 

For example, let’s say you want to find a correlation between education and income. You’d expect people with higher levels of education to earn more, right? 

Well, what if it’s the other way around? What if people with higher income are only more college-educated because they can afford it and lower-income people can’t?

Strategy to Control for Reverse Causation

Here are some ways to prevent and mitigate the effect of reverse causation:

• Longitudinal study

A longitudinal study follows the same individuals or groups over time. This allows researchers to see how changes in one variable (e.g., education) are associated with changes in another variable (e.g., income) over time.

• Instrumental Variables Analysis

Instrumental variables analysis is a statistical technique that estimates the causal effect of a variable when there is reverse causation.

Real-World Applications

Causal research allows us to identify the root causes of problems and develop solutions that work. Here are some examples of the real-world applications of causal research:

• Healthcare Research:

Causal research enables healthcare professionals to figure out what causes diseases and how to treat them.

 For example, medical researchers can use casual research to figure out if a drug or treatment is effective for a specific condition. It also helps determine what causes certain diseases.

Randomized controlled trials (RCTs) are widely regarded as the standard for determining causal relationships in healthcare research. They have been used to determine the effects of multiple medical interventions, such as the effectiveness of new drugs and vaccines, surgery, as well as lifestyle changes on health.

• Public Policy Impact

Causal research can also be used to inform public policy decisions. For example, a causal study showed that early childhood education for disadvantaged children improved their academic performance and reduced their likelihood of dropping out. This has been leveraged to support policies that increase early childhood education access.

You can also use causal research to see if existing policies are working. For example, a causal study proves that giving ex-offenders job training reduces their chances of reoffending. The governments would be motivated to set up, fund, and mandate ex-offenders to take training programs.

Understanding causal effects helps us make informed decisions across different fields such as health, business, lifestyle, public policy, and more. But, this research method has its challenges and limitations.

Using the best practices and strategies in this guide can help you mitigate the limitations of causal research. Start your journey to seamlessly collecting valid data for your research with Formplus .

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Study guides for every class, that actually explain what's on your next test, causal research designs, from class:.

Causal research designs are structured approaches used to determine whether a specific change in one variable directly causes a change in another variable. This type of research is crucial for identifying cause-and-effect relationships, helping marketers understand how different factors impact consumer behavior and decision-making. By manipulating independent variables and observing the resulting changes in dependent variables, causal research provides valuable insights that inform marketing strategies and tactics.

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5 Must Know Facts For Your Next Test

• Causal research designs often employ controlled experiments, where variables are systematically manipulated to establish cause-and-effect relationships.
• Random assignment is a key feature of causal research, ensuring that participants are placed into different groups in a way that minimizes bias.
• This type of research can be conducted in various settings, including laboratories, field experiments, or even through online platforms.
• Causal research helps businesses make informed decisions by providing evidence on how marketing strategies will impact sales or consumer engagement.
• Limitations of causal research include challenges in establishing true causality and the potential for external factors to influence results.

Review Questions

• Causal research designs focus on determining cause-and-effect relationships by manipulating independent variables and observing changes in dependent variables. In contrast, correlational studies only examine the relationship between two variables without manipulating them, making it difficult to establish causation. While correlation can indicate a relationship, it does not imply that one variable directly influences the other, which is a key aspect of causal research.
• Random assignment is crucial in causal research designs because it helps ensure that participants are evenly distributed across experimental conditions. This minimizes biases and confounding variables that could skew results. By randomly assigning subjects, researchers can more confidently attribute any observed effects to the manipulation of the independent variable rather than other external factors, thereby enhancing the validity and reliability of their findings.
• Ethical considerations in causal research designs include informed consent, potential harm to participants, and maintaining confidentiality. Researchers must ensure that participants understand the nature of the study and any risks involved. Ethical concerns may lead to modifications in study design or participant selection, impacting outcomes by restricting certain manipulations or limiting generalizability. Striking a balance between rigorous causal analysis and ethical integrity is essential for producing valid, trustworthy results.

Related terms

Independent Variable : A factor that is manipulated or changed in an experiment to observe its effects on the dependent variable.

Dependent Variable : The outcome factor that researchers measure in an experiment to see how it is affected by changes in the independent variable.

Experimental Design : A systematic approach to conducting experiments that includes the planning and structuring of the research process to test hypotheses effectively.

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Causal Research: The Complete Guide

by Amplitude Marketing | Feb 23, 2023 | Digital Marketing

As we grow up, all humans learn about cause and effect. While it’s not quite as nuanced as causal research, the concept is something our brains begin to comprehend as young as 18 months old. That understanding continues to develop throughout our lives.

In the marketing world, data collection and market research are invaluable. That’s where causal research, the study of cause and effect, comes in.

First-party data can help you learn more about the impact of your marketing campaigns, improve business metrics like customer loyalty, and conduct research on employee productivity. In this guide, we’ll review what causal research is, how it can improve your marketing efforts, and how to conduct your research.

Table of Contents

• What is causal research?
• The Benefits of Causal Research

Causal Research Examples

• How to Conduct Causal Research

Once your team has conducted causal research, your marketers will develop theories on why the relationship developed. Here, your team can study how the variables interact and determine what strategies to apply to future business needs.

Companies can learn how rebranding a product influences sales, how expansion into new markets will affect revenue, and the impact of pricing changes on customer loyalty. Keep in mind that causality is only probable, rather than proven.

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Causal Research – Meaning, Explanation, Examples, Components

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Causal research can be defined as a research method that is used to determine the cause and effect relationship between two variables. This research is used mainly to identify the cause of the given behavior. Using causal research, we decide what variations take place in an independent variable with the change in the dependent variable.

Table of Contents

Meaning and explanation of causal researches

The meaning of causal research is to determine the relationship between a cause and effect. It is also known as explanatory research. A variation in an independent variable is observed, which is assumed to be causing changes in the dependent variable. The changes in the independent variable are measured due to the variation taking place in the dependent variable.

To get the accurate output, other confounding variables that might influence the results are kept constant while creating the data or are controlled using statistical methods. The nature of causal research is very complicated as a researcher can never be sure that no other hidden variables are influencing the causal relationship between two variables. For example, when a company wants to study the behavior of their consumers towards the changing price of their goods, they use causal research.

They might test the behavior of customers depending on different variables. Still, they can never be sure as there can be some hidden variables that might affect the decisions of customers. For instance, no matter how much caution you to take to get the accurate results but there can always be a few psychological considerations that a consumer might be influencing the concerns of the customer even when he is not aware.

The cause and effect relationship between two variables can only be confirmed if causal evidence exists that support the relationship.

The following are the three components for causal evidence

1. Non-Spurious association

The correlated variation between two variables can only be valid if there is no other variable related to both cause and effect.

2. Temporal sequence

A cause and effect can exclusively be connected if the cause has taken place before the occurrence of the effect. For example, it is not right to assume the cause of a dip in sales was the new entrants in the market when sales were already decreasing before the entrance of new entrants.

3. Concomitant variation

Concomitant variation is referred to as the quantitative change occurred in effect is only because of the quantitative change happened in the cause. That means the variation taking place between two variables must be systematic.

For example, if a company does not put effort into increasing sales by hiring skilled employees or by providing training to the employees, then the credit of an increase in sales can’t be given to the recruitment of experienced employees. There will be other causes which caused an increase in sales.

Advantages of causal researches

• Causal research helps identify the causes behind processes taking place in the system. Having this knowledge helps the researcher to take necessary actions to fix the problems or to optimize the outcomes.
• Causal research provides the benefits of replication if there is a need for it.
• Causal research helps identify the impacts of changing the processes and existing methods.
• In causal research, the subjects are selected systematically. Because of this, causal research is helpful for higher levels of internal validity.

Disadvantages of causal research

• The causal research is difficult to administer because sometimes it is not possible to control the effects of all extraneous variables.
• Causal research is one of the most expensive research to conduct. The management requires a great deal of money and time to conduct research. Sometimes it costs more than 1 or 2 million dollars to test real-life two advertising campaigns.
• One disadvantage of causal research is that it provides information about your plans to your competitors. For example, they might use the outcomes of your research to identify what you are up to and enter the market before you.
• The findings of causal research are always inaccurate because there will always be a few previous causes or hidden causes that will be affecting the outcome of your research. For example, if you are planning to study the performance of a new advertising campaign in an already established market. Then it is difficult for you to do this as you don’t know the advertising campaign solely influences the performance of your business understudy or it is affected by the previous advertising campaigns .
• The results of your research can be contaminated as there will always be a few people outside your market that might affect the results of your study.
• Another disadvantage of using causal research is that it takes a long time to conduct this research. The accuracy of the causal research is directly proportional to the time you spend on the research as you are required to spend more time to study the long-term effects of a marketing program.
• Coincidence in causal research is the biggest flaw of the research. Sometimes, the coincidence between a cause and an effect can be assumed as a cause and effect relationship.
• You can’t conclude merely depending on the outcomes of the causal research. You are required to conduct other types of research alongside the causal research to confirm its output.
• Sometimes, it is easy for a researcher to identify that two variables are connected, but to determine which variable is the cause and which variable is the effect is challenging for a researcher.

Examples of Causal Research

• To test the market for a new product by collecting data about its sales potential.
• To check the performance or effectiveness of a new advertising campaign to decide whether to continue it or not.
• To measure the improvement in the performance of employees after providing them training on a new skill.
• To examine the effects of re- branding initiatives based on the level of loyalty of customers.

Liked this post? Check out the complete series on Market research

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About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Understanding  Causal Research

Are you a business owner, marketer , or researcher looking to understand the relationship between cause and effect in your market ? Causal research may just be what you need to connect the dots. In this post, we'll explore what causal research is, how it works, and why it's important for businesses today.

What is Causal Research?

Causal research is a type of market research that aims to establish cause-and-effect relationships between different variables. It involves collecting and analyzing data from various sources to determine whether a particular factor directly influences another. This type of research is often used in business settings to identify the reasons behind consumer behavior , product performance , or market trends .

How Does Causal Research Work?

Causal research typically involves a four-step process:

Formulating a hypothesis: The first step is to develop a hypothesis based on prior knowledge or observations. This hypothesis is usually a statement that reflects the relationship between two variables.

Designing the study: Once the hypothesis is formed, researchers design an experiment or survey to test it. This step involves selecting a sample size , defining variables, and determining how data will be collected and analyzed.

Collecting data: Researchers collect data from various sources using surveys , experiments or other methods. The data collected should be reliable and accurate.

Analyzing results: Once data has been collected, researchers analyze it using statistical modeling and other quantitative research techniques to determine whether the hypothesis was correct or not.

Why Is Causal Research Important?

Causal research is essential for businesses because it helps them understand why things happen in their market. It allows them to make informed decisions based on real data rather than assumptions or guesswork. By using causal analysis , companies can identify what drives consumer behavior and what factors contribute to product success or failure.

What are Examples of Causal Research?

Some examples of causal research include:

• A company testing a new ad campaign to see whether it increases sales
• A retailer changing the price of a product to see how it affects demand
• An online retailer testing different website layouts to see which one leads to more conversions

What is the Role of Data Analytics in Causal Research?

Data analytics plays a critical role in causal research. It allows researchers to collect, organize and analyze large amounts of data quickly and efficiently. By using statistical modeling and other data analysis techniques, researchers can uncover patterns and correlations that may not be immediately visible.

How is Causal Research Different from Correlational Research?

While both causal and correlational research aim to establish relationships between variables, they differ in their approach. Correlational research looks for relationships between variables without necessarily identifying a cause-and-effect relationship. On the other hand, causal research seeks to establish cause-and-effect relationships between variables.

References:

• Babbie, E. (2016). The practice of social research. Cengage Learning.
• Hair Jr, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2019). Multivariate data analysis. Cengage Learning.
• Malhotra, N.K. (2013). Basic marketing research : Integration of social media . Pearson.
• Rubin, D.B. (2005). Causal inference using potential outcomes: Design, modeling, decisions (with discussion and rejoinder). Journal of the American Statistical Association, 100(469), 322-331.
• Shadish Jr, W.R., Cook Jr, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company.

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Causal Research

This article covers meaning, importance, types & example of Causal Research from marketing perspective.

What is Causal Research?

Causal research is a methodology to determine the cause underlying a given behavior and to find the cause and effect relationship between different variables. It seeks to determine how the dependent variable changes with variations in the independent variable.

The results obtained may not be very straight forward because, more often than not, the variability will be a factor of more than one variable. Therefore , while varying one variable, the other variables need to be held constant.

Importance of Causal Research

Causal research has a lot of importance in the field of business. Marketers and businesses use causal research to see impact of their changes to the business model or products on the customers and market. There are many instances where causal research can help organizations to determine cause and effect relationship between various variables like sales, revenue, expenses, products, variations. Let us assume that a company wants to see the impact of change in the attributes of a product like size, color, features on the sales of the product. Another scenario can be introducing a new variant of a car and its impact of additional sales. Causal research proves to an excellent methodology to determine that relationship between those variables and give valuable insights.

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Causal Research Types

This type of research can take two forms:

Experimental

The research performs structured experiments to vary one variable and find the effect on the behavior/end result. In marketing it can be test marketing or focus group studies to determine the cause and end result.

Simulation based

This uses mathematical formulae and statistics to simulate real life scenarios through modeling. Trend analysis based on historical customer sales data can give insights into behavior and can help drive useful decisions for the organization. 

Causal Research Example

For example, a marketer may want to determine the cause of dip in sales. He would test the sales against various parameters like selling price, competition, geography etc.

Also another example can be change in the customer experience in digital channels and its impact on the overall customer scores.

Hence, this concludes the definition of Causal Research along with its overview.

This article has been researched & authored by the Business Concepts Team which comprises of MBA students, management professionals, and industry experts. It has been reviewed & published by the MBA Skool Team . The content on MBA Skool has been created for educational & academic purpose only.

Browse the definition and meaning of more similar terms. The Management Dictionary covers over 1800 business concepts from 5 categories.

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• → The 8 types of market research and ho...

The 8 types of market research and how to use them

There are eight types of marketing research you can try to stay ahead of the competition. Learn more about marketing research methods and how to use them.

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“If you keep doing what you’ve always done, you’ll keep getting what you’ve always got.”

Doesn’t sound too threatening if you’ve always been successful, right?

Continuing to do what you’ve always done means you’ll fall behind—and probably fade to darkness—to where all the forgotten brands go.

Take Kodak. They were a major player in photography for decades—remember? When digital photography boomed, Kodak kept doing what they always did. Their business floundered and people forgot about them. Well, everyone apart from Pitbull.

Now, look at Fujifilm, one of Kodak’s biggest competitors. They did the opposite and looked for ways to apply their expertise in film to the technology of the new millennium instead. Their company is still going strong.

The same goes for research. If you’re doing the same old types of market research, speaking to the same old people, and doing the same old tired surveys—you’re already behind.

How do you decide what kind of market research you need to do? It all comes down to what you need to know and what your business goals are.

In this article, we’ll explain the various types of market research you can use to solve issues and challenges in your business. We’ll throw you a freebie, too, and provide some market research tips about when to use each strategy.

Let’s get you ahead of the curve.

1. Brand research

Brand research helps with creating and managing a company’s brand, or identity. A company’s brand is the images, narratives, and characteristics people associate with it.

When to use it

Brand research can be used at every stage in a business’s lifecycle, from creation to new product launches and re-branding. There are at least seven types of brand research:

Brand advocacy: How many of your customers are willing to recommend your brand?

Brand awareness : Does your target market know who you are and consider you a serious option?

Brand loyalty: Are you retaining customers?

Brand penetration: What is the proportion of your target market using your brand?

Brand perception : What do people think of as your company’s identity or differentiating qualities?

Brand positioning: What is the best way to differentiate your brand from others in the consumer’s mind and articulate it in a way that resonates?

Brand value: How much are people willing to pay for an experience with your brand over another?

How to do it

A researcher will use several types of market research methods to assess your and your competitors’ strengths and weaknesses. Generally, they will conduct competitor research, both qualitative and quantitative, to get a picture of the overall marketplace. Focus groups and interviews can be used to learn about their emotions and associations with certain brands.

Market research surveys are useful to determine features and benefits that differentiate you from competitors . These are then translated into emotionally compelling consumer language.

2. Campaign effectiveness

This type of market research is designed to evaluate whether your advertising messages are reaching the right people and delivering the desired results. Successful campaign effectiveness research can help you sell more and reduce customer acquisition costs.

It’s estimated people see up to 5,000 advertising messages each day. That means attention is a scarce resource, so campaign effectiveness research should be used when you need to spend your advertising dollars effectively.

Campaign effectiveness research depends on which stage of the campaign you use it in (ideally, it’s all of them!). Quantitative research can be conducted to provide a picture of how your target market views advertising and address weaknesses in the advertising campaign.

3. Competitive analysis

Competitive analysis allows you to assess your competitors’ strengths and weaknesses in the marketplace, providing you with fuel to drive a competitive advantage.

No business exists in a vacuum—competitive analysis is an integral part of any business and market plan. Whether you’re just getting started, moving into a new market, or doing a health check of your business, a competitive analysis will be invaluable.

A researcher will typically choose a few of your main competitors and analyze things like their marketing strategy, customer perceptions, revenue or sales volume, and so on.

Secondary sources such as articles, references, and advertising are excellent sources of competitive information; however, primary research, such as mystery shopping and focus groups, can offer valuable information on customer service and current consumer opinions.

4. Consumer insights

Consumer insights research does more than tell you about who your customers are and what they do. It reveals why customers behave in certain ways and helps you leverage that to meet your business goals.

Knowing your customers deeply is integral to creating a strategic marketing plan. This type of market research can help you anticipate consumer needs, spark innovation, personalize your marketing, solve business challenges, and more.

Consumer insights research should be specific to your business—it’s about getting to know your target audience and customers. Various market research methods can be used, such as interviews, ethnography, survey research, social monitoring, and customer journey research.

Here are some of the characteristics you should understand through consumer insights research:

Purchase habits

Interests, hobbies, passions

Personal and professional information

How they consume media and advertising

5. Customer satisfaction research

Customer satisfaction research is a type of market research that measures customers’ experiences with products or services, specifically looking at how those meet, exceed, or fail to live up to their expectations.

Customer satisfaction is a strong indicator of customer retention and overall business performance. Successful customer satisfaction research should help you understand what your customers like, dislike, and feel needs improvement. You can use this type of market research to look at the quality and design of products, speed and timeliness of delivery, staff and service reliability, knowledge, and friendliness, market price, and value for money.

There are several ways to measure customer satisfaction, most commonly using surveys. An NPS or Voice of the Customer Survey can help you measure customer loyalty. Customer Effort Scoring measures how satisfied people are with customer service or problem resolution. CSAT is any survey that measures customer satisfaction , typically measured using Likert scale surveys . They can be conducted at different points in the customer experience, allowing deeper insight into that moment.

6. Customer segmentation research

Customer segmentation studies aim to divide markets or customers into smaller groups or personas with similar characteristics to enable targeted marketing. By understanding how people in each category behave, you can understand how each influences revenue.

Customer segmentation research is best used if you’re ready to give customers individualized experiences. Not every customer in your target market is the same. The more you understand each specific persona, the easier it is to focus on delivering personalized marketing, build loyal relations, price products effectively, and forecast how new products and services will perform in each segment.

Market researchers use four characteristics to segment customers.

Demographics: demographic information such as age, gender, family status, education, household income, occupation and so on

Geography: where people live, from cities and countries to whether they are city dwellers or suburbanites

Psychographics: socioeconomic status, class, lifestyle, personality traits, generation, interests, hobbies, etc.

Behavior: brand affinity, consumption and shopping habits, spending, etc.

A researcher will identify your current customers and collect data about them through various market research methods, such as surveys, database research, website analytics, interviews, and focus groups. The aim is to gather as much information as possible.

7. Product development

Market research for product development involves using customer knowledge to inform the entire process of creating or improving a product, service, or app and bringing it to market.

Innovation is hard work. A quick Google will tell you that 80–95% of new products fail every year. Conducting market research for product and app development helps minimize the risk of a new product or change going bust as it enters the market. There are three stages where you can use market research:

Conception: The moment you’re thinking about adding something new, market research can find market opportunities and provide insights into customer challenges or their jobs-to-be-done, so you can find a way to fill the gap.

Formation: Once you have an idea, market researchers can help you turn it into a concept that can be tested. You can learn more about strategizing pricing, testing advertising and packaging, value proposition, and so on.

Introduction: Market research can help you gauge attitudes toward the product once it’s in the market and adapt your messaging as it rolls out.

Keep making the product better or find opportunities to introduce it to new markets.

Product development research will utilize different market research methods, depending on the goal of the research. A researcher could present focus groups with product concepts and listen to their opinions, conduct interviews to learn more about their pain points, or perform user testing to see how they interact with an app or website.

8. Usability testing

Usability testing is concerned with understanding how customers use your products in real time. It can involve physical products, like a new blender, or digital products like a website or app.

Usability testing is helpful when you need to detect problems or bugs in early prototypes or beta versions before launching them. It typically costs far less to test a product or service beforehand than to pull a flawed product off the shelves or lose sales because of poor functionality.

There are several types of usability tests, which vary based on whether you’re testing a physical or digital product.

Journey testing involves observing the customer experience on an app or website and monitoring how they perform. This type of study can be done online

Eye tracking studies monitor where people’s eyes are drawn. Generally, they are conducted on websites and apps, but can also be done in stores to analyze where people look while shopping

Learn ability studies quantify the learning curve over time to see which problems people encounter after repeating the same task

Click tracking follows users’ activity on websites to evaluate the linking structure of a website

Checklist testing involves giving users tasks to perform and recording or asking them to review their experience

Combining types of market research with Typeform

When it comes to market research, you need to ask yourself what business challenge or question you’re trying to address. Then, select the appropriate methods and tools, such as market research automation , to simplify your process.From there, the world of useful data and actionable insights will open to you.

About the author

We're Typeform - a team on a mission to transform data collection by bringing you refreshingly different forms.

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Chapter 4 explores trends in causal/experimental research design, usually quantitative data used in a structured manner to test a hypothesis. Approaches in typical marketing research texts are briefly covered, including the logic and structure of experiments, sources of bias, test markets, and experimental designs, including quasi-experiments. Although existing data often aren’t appropriate for experiments, ongoing collection programs can be employed, using observation or communication techniques. New approaches that can unobtrusively test responses in both the real world (field experiments) and virtual environments (laboratory experiments) are included. Extended examples cover advertising testing, pricing variation and dynamic pricing, shopping environments and beta tests. Important trends such as ongoing mini-experiments in day-to-day operations, individualized experiments, and unobtrusive testing are all covered.

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The Complete Guide to Marketing Work Management: Get Started With the Right Tools and Processes

By Joe Weller | October 24, 2024

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Marketing work management aligns efforts with business goals for better results. We’ve gathered expert insights and useful templates to help you choose the right tools and enhance your marketing management.

Included this article, you’ll find the following:

• How to implement marketing work management
• Downloadable marketing work management starter kit
• Marketing software research comparison matrix
• Chart of marketing priorities and solutions by role

What Is Marketing Work Management?

Marketing work management means planning and carrying out marketing tasks and projects. This includes preparing campaigns, conducting research, and creating content, along with basic project management tasks. Teams stay organized, track progress, and complete tasks on time and within budget.

As the world grows more connected, managing marketing work is more important than ever. Increasing competition and wider reach mean marketing initiatives are often high risk but also offer high rewards. It is important to manage investments you make in your marketing department just as effectively as any other department.

One important part of marketing work management is marketing project management, which focuses on executing and overseeing specific marketing projects. Learn more in this  guide to marketing project management .

Benefits of Marketing Work Management

There are many benefits of marketing work management. To start, you can more easily track progress and keep stakeholders informed without constant updates. It also helps you manage resources by clarifying task assignments and workloads. By automating routine tasks, marketing teams can focus more on creative work and building a brand.

Here are the top benefits of strong marketing work management:

• Improved Transparency and Visibility: Work management tools allow you to easily share progress and status updates with stakeholders at all levels. Use marketing work management software to provide real-time dashboards, ensuring updates are visible without needing manual reporting.
• Better Resource Management:  By tracking resources in one place, it’s easier to assign tasks and monitor who is doing what. Use past project data to balance workloads and identify which departments are at capacity.
• Better Communication: Work management systems streamline communication by making status updates and project plans accessible to everyone. With the right software, stakeholders can view progress and changes on their own, instead of relying on frequent check-ins.
• Digital Asset Management:  Marketing work management platforms offer robust digital asset management features, ensuring your team always has exactly the files they need. Organize assets in a centralized library so teams can quickly access past files for reference and use them as templates for new projects. Learn more in this essential guide to digital asset management.
• Flexibility:  Having a work management system in place allows for flexibility when unexpected issues arise. “You are going to have to think on your feet to sort and solve challenges, and the train may be leaning to one side, but it's not going to fall off the track because the guardrails you established are keeping you in with the wheels still moving,” says Berry.
• Managing Cross-Functional Teams:  A good work management platform centralizes all project information, making it easier to manage cross-functional teams. Let team members access and manage their tasks in a shared digital space to reduce the need for frequent, time-consuming email collaboration. Learn more in this  guide to increasing efficiency with marketing team collaboration .
• Automation of Repetitive Tasks: Work management systems automate repetitive work, such as sending status updates or reminders. Use the built-in automation features that take care of these tasks, freeing up your team to do the work that really matters. Find everything you need to know about workflow management in this  guide to marketing workflows .
• Prioritize Creative Development:  Creatives need the time and space to be creative. By simplifying work management with the right software and tools, you can spend less time on administrative tasks and more on the creative work that drives your brand forward.
• Scaling Content Production:  Work management tools make it easier to scale your campaigns and content production. Use past projects as templates for new ones, keeping the strategies that work and customizing them for new campaigns.
• Deliver Better Customer Experiences:  A work management platform improves both employee and customer experiences. Give your customers their own limited access to view the status of your work directly, eliminating the need for frequent status update requests and encouraging transparency and trust.
• Team Member Satisfaction:  Proper work management makes it easier for your team to do their jobs, reducing stress and dissatisfaction. Track workload balance and use project templates to streamline tasks, which boosts satisfaction and reduces employee turnover.

Marketing Work Management Examples

Marketing work management tools help companies organize campaign planning, manage assets, and speed up launches. For example, PTC cut its planning time by 30 percent using automation. McLaren also sped up its work by automating uploads and storage with a digital asset tool.

Many companies have found success in part because of successful marketing work management. With the right software and tools, companies can speed up marketing campaign planning and launches. They can also more effectively manage the assets to run an ongoing campaign.

Take PTC, for example, which reduced the time needed for campaign planning by 30 percent after adopting a marketing work management tool. By automating key parts of its intake request process and unifying its disparate tech stack, PTC was able to get work done like never before.

Another great example is McLaren, which found that using a marketing work management tool allowed it to deliver assets to its partners almost immediately, speeding up the creation and distribution of marketing campaign materials. Using a digital asset management tool allowed McLaren to automate its media uploads and storage, making those assets available for use in record time.

How to Implement Marketing Work Management

To implement marketing work management, start by identifying your goals and determining who will be responsible for achieving them. Audit your workflows and ensure they’re working as well as they can. Use automation tools made for marketing management to increase visibility, organize work, and save time.

“My advice, in the simplest terms, is to find out what matters to people,” advises  Jillian Knoepfel , Manager of Content Operations at Smartsheet. “Sit with the various teams, managers, leaders, and stakeholders to figure out what information they need to have in order to do their job more easily and optimally — whether that’s executing on a task or making an informed decision. There are a lot of hands involved in and impacted by the successful outcome of a marketing project, and if you can find out what each person or team involved needs and how to effectively get it in their hands, you’ve won half the battle.”

Explain the benefits of your new software, such as how it will increase efficiency, improve collaboration, and provide better visibility into project progress. Establish training sessions to introduce your team members to the software's features and functionalities, and address their questions or concerns as they come up. 

“Once you have an understanding of what the various key players care about, you can more effectively pan out to see the bigger picture and help to ensure all those dots get connected for a more efficient and cohesive operation,” says Knoepfel. “In addition, it’s helpful to learn what the gaps and pain points are that the stakeholders and teams see and experience. Removing barriers and closing gaps will enable the team to focus on doing what they do best, ultimately creating a highly effective marketing team.”

Introduce new workflows gradually, and integrate them into your existing processes to minimize disruption. By involving your team throughout the process and emphasizing the potential for positive impact on their own work, you can help to foster an environment that fully embraces the new system of work management.

Marketing Work Management Priorities and Solutions by Role

Marketing work management lifecycle.

The marketing work management lifecycle includes seven phases: planning, creation, review and approval, launch, analysis, optimization, and iteration. 

Here’s a breakdown of each phase in the marketing work management lifecycle and what it includes:

• Planning: The first phase includes identifying goals, performing market research, and determining the audience and platform for your marketing project.
• Creation: The next phase involves the creation of the assets required for your marketing campaign.
• Review and Approval: Once the team has created the assets outlined in the plan, stakeholders should review them for final approval.
• Launch:  Next, launch the campaign and monitor its progress.
• Analysis: After the campaign launch, collect ongoing data and determine if any changes are required.
• Optimization: Make any necessary tweaks to the project, add or remove assets, or execute new phases of the campaign.
• Iteration:  To inform future projects, keep a record of the things that worked and the things that didn’t.

Marketing Work Management Starter Kit

Download the Marketing Work Management Starter Kit

We’ve created this free marketing work management starter kit so that you can start handling your marketing work more easily. With the help of our experts, we’ve collected some of our best marketing templates for Microsoft Word and Excel — including our new marketing work management software research comparison matrix — into one downloadable resource.

Included in this kit, you’ll find:

• A  marketing work management software research comparison matrix for Excel  to help you organize and compare your research on different marketing work management platforms.
• A  marketing campaign brief template for Microsoft Word  to create an overview of your marketing plan to share for approval from stakeholders before launch.
• A  marketing project plan template for Microsoft Word  to create detailed project plans for your marketing campaign.
• An  annual content calendar template for Excel  to keep track of campaign project deadlines and ongoing campaign schedule information.
• A   marketing budget plan template for Excel  to plan and track campaign budgets.
• A  marketing campaign report template for Excel  to create detailed charts and graphs that display campaign results.
• A   simple RACI matrix template for Excel  to keep your team accountable for their tasks, who they report to, and who is responsible for what parts of each project.

Marketing Work Management Best Practices

Experts suggest best practices for managing marketing work, such as using the right tools, staying organized, and focusing on teamwork. Software tools and templates can make workflows easier, while setting clear goals helps keep projects on track. Being flexible and adjusting to changes can also make your project more successful.

Here are the best practices recommended by our marketing experts:

• Use Marketing Work Management Tools: There are numerous tools on the market to make marketing work management easier. Digital asset managers can help organize and store your assets, while project management software makes it easy to schedule and assign tasks. Even the most basic tools can sometimes  enhance the communication and collaboration within a marketing team . “When it comes to getting things done, don't sleep on the basics,” suggests Berry. “There are so many amazing pieces of software out there that can help you reach your goals!”
• Stay Organized: A critical component of successful work management is organization. Stay organized to enhance your existing processes and workflows. “Your tools won't solve problems if you inherently lack organization or (as I learned the hard way earlier in my career) if you don't have a clearly defined workflow,” warns Berry. “Know your own processes — your file naming conventions, etc. — and put those to work within your tools for maximum output. And don't forget to make sure your team knows these and has them documented as well.”
• Clearly Define Your Goals:  One of the most important things you can do is to clearly define your goals and how you will measure them. Set SMART goals that are specific, measurable, achievable, relevant, and time-bound. To learn more, see this  guide to writing effective SMART goals . 
• Stick With It: Give new tools an honest chance to show you what they can do. “I think many people get stuck when they have to set up new tools and processes because, as you craft them, it all feels so rigid,” says Berry. “In my experience, what ends up happening is anything but that. I find that the tools make me even more flexible.”
• Don’t Forget the Intake Process:  A critical piece of marketing and creative work management is simplifying the intake process. “Make it easy for requesters to tell you what they need, and that will help to set expectations early,” explains Knoepfel. “For example, if a blog post is selected on the intake form, then additional related fields should appear, such as: Who is the intended audience? When is the expected launch date? Is this connected to a company OKR?”
• Analyze Your Data:  Use your marketing work management software to track metrics such as engagement and performance, and use that data to inform decisions about future projects. Learn from your successes just as much as from your mistakes.
• Prioritize Integration: Use tools that integrate well with the ones you already use. “Having tools and systems that speak to each other makes the process itself smoother,” Knoepfel explains. “A smoother, more integrated process is going to make team onboarding much easier. If it feels forced or disjointed, the team will (understandably) end up falling back on old habits and processes, so the tools will set the foundation for the process and the team.”
• Perform Market Research:  Take the time to do thorough market research on your competitors and peers. Learn from what they’re doing and apply the strategies that work for them, while noting what isn’t working so you know what to do differently.
• Collaborate Across Teams:  Marketing projects often involve more than the marketing team. Include other teams in your planning and strategy meetings when necessary, and be sure to get the relevant approvals along the way.
• Use Templates: Templates help ensure that each document includes all the necessary information. They’re also a great tool for training and maintaining consistency. “Templatize things as much as possible,” suggests Berry. “I think of it the way that some people have a work uniform and that helps stop their decision fatigue from happening in the morning. Figure out what works for your team, make a template, and share it out so that everyone is working in tandem.”
• Stay Flexible:  Above all else, maintain flexibility. Requirements and deadlines will change, so control what you can and pivot when necessary.

What Is Marketing Work Management Software?

Marketing work management software helps manage marketing teams and projects more easily. It provides tools to plan, organize files, manage proofs, share updates, assign tasks, and create standard workflows. It also improves team communication and ensures marketing efforts match business goals, making everything more efficient.

Liz Miller, VP and Principal Analyst at  Constellation Research , shares her thoughts: “First and foremost, marketing’s mandate has shifted from the management of the Ps (product, place, price, and promotion) to optimization of the Rs (revenue, relationships, and reputation). This expands the types of initiatives, campaigns, data, operations, metrics, projects, and work that need to be tracked.”

Often,  marketing work management software is specialized project management software. For example, projects that require a content schedule or content calendar and collaboration with internal and external marketing teams need a more specialized combination of tools than more traditional, internal projects.  

A good marketing work management solution should include traditional project management tools such as project planning and tracking, dashboards, task assignments, capacity planning, and collaboration, as well as marketing and creative-specific features such as digital asset management, proofing, and robust file-sharing capabilities. 

Marketing Work Management Tools vs. Traditional Project Management Tools

Marketing work management tools are tailored to the specific needs of marketing professionals. They might include features such as digital asset management, content calendars, market research tools, campaign tracking, and social media management. Marketing teams often use them alongside traditional project management tools, such as communication platforms and project planners.

Marketing Work Management Platforms vs. Work Management Platforms

Marketing work management platforms include a set of features that are ideal for managing marketing work. They combine traditional work management tools — such as project planning, visualization, and collaboration — with marketing tools such as digital asset managers, content calendars, and campaign trackers.

Learn more about the basics of good work management in this  all-in-one guide to traditional work management .

The Role of AI in Marketing Work Management

As the marketing landscape changes, AI helps by making tasks easier and using resources better. AI tools can track projects, automate repetitive tasks, assign work, and check team performance. Adding AI to marketing work management helps teams work together, save time, and make better decisions.

“Work management for marketing now needs to include how data, automations, and processes converge, including where and how AI agents are being deployed,” Miller explains. Tools you choose should include AI features that make your job easier and save time.

“It is a good exercise to take a step back to consider what the work of marketing truly is and reverse engineer what marketing work management should be focusing on. This is especially critical as we start to plan out how AI co-pilots and agents have started to become part of the marketing team,” explains Miller.

Here are some of the ways that Miller says AI can help boost the efficiency of your marketing work:

• AI Application Management and Tracking: Including hybrid workforce analysis and task management can save your team time and money.
• AI Deployments for Smart Talent Resourcing: Use AI for schedule management, smart task assignment, task automation, and autonomous task completion for specific workflows and automations.
• Expansion of Collaboration Channel Choice: For assets, videos, or asynchronous communication, use AI to perform analyses of conversations and wrap-up summaries.

How to Compare Marketing Work Management Systems

To compare marketing work management systems, start by using free trials to test different tools on small projects. Take notes on features, usability, and how well each system integrates with your current tools. Ask coworkers for feedback, and research what tools are popular in your industry and why.

It can be difficult to compare marketing work management systems at a glance. We’ve collected the following advice from experts on how to approach your research to help you get started:

• Save Money With Free Trials:  Most marketing work management software offers a free trial of their platform to see if it’s a good fit. Use those free trials to test new toolsets before making an investment. “Budgets and initiative-level spend will, of course, be tracked and managed across projects and programs, but visibility into revenue will also be critical, as margin — not just gains and growth — is key to profitable outcomes. Increasingly, CMOs want to track the cost of engagement as part of tracking the efficacy and impact of work,” says Miller.
• Take Notes: Don’t just use a free trial; take detailed notes on what you like and don’t like about a tool. Try this marketing work management software research comparison matrix to keep track of the offerings of each of your options, as well as which features are priorities for your team.
• Manage a Small Project With a New Tool: Berry says that a common problem she’s faced with new systems in the past is not exploring features enough to see what a tool can do. To get the most of your free trial, give it a test run on a small project and see how it performs. This is the best way to make sure that you’re using the tool to its full potential. “These days, I no longer doubt that these kinds of tools will save me and my team a ton of time and effort,” says Berry.
• Compare Your Options:  Most work management tools have only minor differences between the features they offer. Sometimes, the difference comes down to aesthetics versus capabilities. “The flexibility and scale of a work solution can be a real differentiator,” explains Miller. “Instead of a ‘creative-centric’ work management approach that can struggle with customizations and budget- and business-friendly reporting, for a strategic marketer, the focus on the work of business is going to be far more important than a task manager that has more collaboration tools than reporting and predictive capabilities.”
• Ask Your Peers:  There’s a good chance that your colleagues have used marketing work management software before. Ask around and see if anyone has rave reviews about a particular tool.
• Consider Integration: It’s important that any new software works well with the tools you already use and fits seamlessly into your current workflows. The bigger the change, the more challenging the implementation will be. “Using a connected system of tools or products (if you can find a work management platform that has a majority of the capabilities you need, or can integrate with your must-haves, even better) to facilitate the management and flow of marketing work will make your processes more streamlined and reporting more accurate. It’s also a lot easier to get started if it’s all in one place,” explains Knoepfel.
• Perform Market Research: Let your peers do some of the work for you. Find out what tools others in your field are using and why it works for them. Use the internet and message boards to gather feedback on what they prefer and why.

Here are three additional facets of marketing work management software that Miller says are critical:

• Customer Data Source Management: This includes understanding new sources from far outside the marketing ecosystem, such as commerce, service, ERP, partners, and supply chains.
• Data and Segment Scheduling: Consider a shared calendar that extends outside of the marketing org.
• Cross-Functional Collaboration: Including tools for tracking business and your strategic “North Star.”
• True Visibility:  Traditional, hybrid, and fully autonomous workflow visibility is critical.

Marketing Work Management Software Research Comparison Matrix

Download the Marketing Work Management Software Research Comparison Matrix for Excel

We’ve created this marketing work management software comparison matrix to help you keep notes while researching different software options. With space to compare up to three different offerings and the ability to mark features as priorities, this matrix will guide you through the research process with questions about the tool’s work management capabilities, including marketing-specific features such as content calendars and digital asset management. This template is fully customizable, allowing you to add and remove rows and questions to fit your business needs.

Optimize Marketing Work Management With Smartsheet for Marketing and Creative Management

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• Open access
• Published: 02 November 2024

Advances in drug-induced liver injury research: in vitro models, mechanisms, omics and gene modulation techniques

• Kaidi Guo   ORCID: orcid.org/0000-0001-9374-1016 1 &
• Twan van den Beucken 1  

Cell & Bioscience volume  14 , Article number:  134 ( 2024 ) Cite this article

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Metrics details

Drug-induced liver injury (DILI) refers to drug-mediated damage to the structure and function of the liver, ranging from mild elevation of liver enzymes to severe hepatic insufficiency, and in some cases, progressing to liver failure. The mechanisms and clinical symptoms of DILI are diverse due to the varying combination of drugs, making clinical treatment and prevention complex. DILI has significant public health implications and is the primary reason for post-marketing drug withdrawals. The search for reliable preclinical models and validated biomarkers to predict and investigate DILI can contribute to a more comprehensive understanding of adverse effects and drug safety. In this review, we examine the progress of research on DILI, enumerate in vitro models with potential benefits, and highlight cellular molecular perturbations that may serve as biomarkers. Additionally, we discuss omics approaches frequently used to gather comprehensive datasets on molecular events in response to drug exposure. Finally, three commonly used gene modulation techniques are described, highlighting their application in identifying causal relationships in DILI. Altogether, this review provides a thorough overview of ongoing work and approaches in the field of DILI.

Introduction

Drug-induced liver injury (DILI) refers to a series of adverse reactions caused by various pharmaceutical drugs (including prescription and over-the-counter drugs), herbal, and dietary supplements through multiple mechanisms [ 1 ]. DILI is a significant global health concern, with estimated annual incidence rates ranging from 1.3 to 19.1 per 100,000 individuals worldwide, and the prevalence and etiology vary geographically [ 2 ]. DILI typically occurs after a period of several days to months following drug exposure, whereas liver injury resulting from drug overdose presents within hours to days. The symptoms of DILI or hepatotoxicity vary among individuals. In some mild or chronic cases, patients may not experience any symptoms of liver damage, while frequent episodes of acute liver injury are responsible for the development of acute liver failure [ 3 ].

DILI is a safety concern that leads to the termination of clinical drug development programs and is the primary reason for drug withdrawal post-marketing [ 4 , 5 ]. DILI may remain undetected prior to drug approval, and preclinical safety data is insufficient for early prediction in humans [ 6 ]. Furthermore, diagnosing DILI is challenging and relies on obtaining a comprehensive medication history from the patient to rule out other potential causes of liver injury [ 7 ].

The assessment of DILI on an international scale commonly involves the use of CIOMS Roussel-Uclaf Causality Assessment Method (RUCAM) scale, the Maria & Victorino System of Causality Assessment, and the Clinical Diagnostic Scale (CDS) [ 8 ]. However, the scales are prone to instability issues and do not account for all the risk factors in patients. Therefore, it is imperative to obtain dependable and efficacious biomarkers that facilitate the prediction or diagnosis of DILI.

In vitro models to assess DILI

To acquire useful preclinical data DILI screening should be performed in a robust and physiologically relevant model system. Traditional animal models have limited clinical relevance and, more importantly, there are significant species differences in drug metabolism [ 9 , 10 , 11 , 12 ]. Two-dimensional (2D) or three-dimensional (3D) cell models are currently the most widely used tools for developing preclinical drugs and exploring the drug-specific risks of DILI. The application of in vitro models also better comply with the 3R principles. This chapter presents a detailed overview of commonly applied and potentially promising in vitro systems, along with their respective advantages and disadvantages.

HepG2 cells

HepG2 is a hepatocellular carcinoma cell line with high morphology and positive differentiation. This cell line has been generated in the 1970s and has been used in over 34,021 biomedical studies up to date. Yet, there are two important concerns regarding this model. First, although drug metabolism is clearly active in HepG2 cells, drug metabolizing enzymes are not expressed at similar levels compared to primary human hepatocytes (PHHs). This is particularly relevant in studies trying to understand correlations between drug dose and adverse outcome. Therefore, this model is not suitable for determining parameters like lowest-observed-adverse-effect-level (LOAEL) or acceptable daily exposure. The second caveat of HepG2 is the unstable, aneuploid genome, which consists of 50–60 chromosomes. This makes adequate interpretation of functional genomics data generated with HepG2 extremely difficult. Despite these two critical shortcomings, the model itself or data previously generate using this model is still being used in large EU projects EU-ToxRisk, RISK-HUNT3R and ONTOX.

HepaRG cells

The HepaRG cell line was developed as a more biological relevant model for toxicological studies. When fully differentiated, they resemble mature hepatocytes and retain typical liver function [ 13 , 14 ]. HepaRG cells have unique biological characteristics, initially appearing as a narrow and long undifferentiated morphology while expressing biological markers of hepatic progenitor cells at low density. After one week, these cells differentiate into distinct hepatocyte morphology and bile duct-like structures.

In comparison to HepG2, HepaRG cells exhibit elevated levels of CYP activity, including CYP1A2, CYP2B6 and CYP2C9, and express phase II enzymes and membrane transporters when exposed to 2% dimethyl sulfoxide (DMSO) [ 15 ]. Simultaneously, differentiated cells fully express several drug-related nuclear receptors (NRs), such as the constitutive androstane receptor (CAR), pregnane X receptor (PXR) [ 16 , 17 ], and three isoforms (α, γ and β/δ) of peroxisome proliferator-activated receptor (PPAR) [ 17 , 18 ].

Both transcript levels and activity of drug-metabolizing enzymes in HepaRG cells mimic those of primary human hepatocytes (PHHs). HepaRG are more economical, convenient, and show less variation compared to commercial PHHs [ 19 ]. HepaRG is suitable for measuring toxic responses based on xenobiotic metabolism and are a superior substitute for HepG2 in DILI investigations. Additionally, HepaRG can also be used for in vitro studies of targeted drugs in cholestasis and steatosis [ 20 , 21 , 22 ].

However, HepaRG also has drawbacks. High concentrations of DMSO (e.g., 2% DMSO) used for HepaRG cell differentiation may affect the determination of hepatotoxicity results. Rodrigues et al. retained 2% DMSO to maximize the metabolic activity of differentiated HepaRG cells. But they also acknowledged that while DMSO ensures maximal differentiation stages of cells and maintains high expression of CYP, it is also possible to influence the effects of the compounds analyzed [ 23 ]. Verheijen et al. emphasized the ability of DMSO to induce alterations in cellular processes in hepatocytes, and suggested that the use of DMSO should be avoided as much as possible. Even though it is indispensable in biotechnological applications, its concentration should be kept as low as possible [ 24 ]. The good news is that undifferentiated HepaRG cells embedded in a collagen matrix rapidly organize into differentiated HepaRG polarized hollow spheres without DMSO, and this model is particularly suitable for evaluating the effects of drugs on cholestasis and steatosis after long-term treatment [ 25 ].

Furthermore, Rodrigues et al. also mentioned the low levels of certain enzymes involved in drug detoxification and biotransformation in HepaRG cells, such as CYP2D6 and CYP2E1 [ 23 ]. The issue with CYP2E1 is gradually being addressed [ 26 ].

Primary human hepatocytes

During preclinical drug development, in vitro models established based on PHHs are considered the gold standard for evaluating liver drug toxicity. They offer great advantages in the pharmacy realm. PHHs retain the complete morphology of human hepatocytes, maintain consistency with the in vivo environment after isolation, and have biological enzyme content and cofactor levels that mimic typical physiological concentrations [ 27 ].

PHHs exhibit higher metabolic activity compared to any other hepatocyte model and best represent in vivo liver conditions. In contrast to monolayer cultures, PHHs in suspension have been observed to not only more accurately estimate clearance rates within a very short period but also retain robust liver functionality, thus providing a more authentic reflection of DILI scenarios [ 28 ]. However, since liver toxicity typically manifests several hours later, PHHs in suspension cannot maintain viability within the necessary timeframe. PHHs are capable of sustaining functional activity for 24–72 h, usually requiring adhesion to collagen, with monolayer cells showing higher enzyme stability [ 29 , 30 ]. However, after several days of conventional 2D culture, cells gradually lose both their morphology and functionality [ 31 , 32 , 33 ].

Many efforts have focused on prolonging the hepatic phenotype of PHHs in cell culture. It is generally recognized that culturing PHHS in a collagen sandwich culture can sustain the secretion of plasma proteins, enhance the gene expression of energy metabolism, and partially promote CYPase activity [ 34 , 35 , 36 ]. Additionally, Upcyte technology has been applied to establish human hepatocytes with proliferative properties, which has been utilized to clinically assess acute and repeated dose hepatotoxicity [ 37 , 38 ].

In 2019, Xiang et al. found that PHHs showed an average 10-fold down-regulation of multiple important liver specific genes after 24 h of in vitro culture. The main components of the transforming growth factor beta (TGF-β) signaling pathway and epithelial-mesenchymal transition (EMT) inducers were up-regulated, leading to PHHs dysfunction. In order to maintain the correct hepatocyte phenotype during in vitro culturing, five chemicals were employed that work synergistically with each other: FSK (an adenylate cyclase activator), SB43 (a TGF-β inhibitor), DAPT (a Notch inhibitor), IWP2 (a Wnt inhibitor) and LDN193189 (a BMP inhibitor). This protocol effectively maintains the expression of functional genes and key transcription factors of PHHs (such as CEBPA, CREBH, HNF4A, PXR, CAR, CYP3A4, CYP2D6, ARG, UGT1A6, and NTCP), while inhibiting the expression of mesenchymal marker genes [ 39 ]. This is a positive finding that has inspired some researchers to apply this advanced and efficient method to explore the mechanisms of HBV antiviral treatment, and lipid and glucose metabolism [ 40 , 41 ]. Zhong et al. even confirmed that single-layer 5 C-PHHs perform as well as 3D-PHHs and can be more conveniently used for preclinical drug safety assessment [ 42 ].

Another limitation of PHHs is their restricted availability, as this is dependent on scarce organ donations and patient biopsies. In addition, the isolated PHHs are not naïve in terms of exposure and could already be in an unhealthy state.

Hepatocyte-like cells derived from induced pluripotent stem cells

The development of pluripotent stem cell technology nearly two decades ago, has made it possible to generate an unlimited source of patient-derived cell models for disease modeling [ 43 ]. Ever since, various specific cell types have been generated using sophisticated differentiation strategies including hepatocyte-like cells [ 44 , 45 ]. iPSCs are similar to human embryonic stem cells (hESCs) in terms of cell morphology, gene expression, epidermal modification, and proliferation and differentiation capabilities [ 46 ]. Importantly, there are less serious ethical controversies related to be used of iPSCs versus hESCs [ 47 , 48 ].

iPSCs progressively differentiate into hepatocyte-like cells (HLCs) through endoderm induction, liver specificity and maturation. This is achieved by chronologically adding Activin A, Wnt3, bone morphogenetic protein (BMP) and fibroblast growth factor (FGF), dexamethasone and hepatocyte growth factor (HGF) as supplements [ 49 , 50 ]. iPSC-derived HLCs exhibit several liver functions, including of serum protein secretion, urea synthesis, glycogen storage, and CYP enzyme expression [ 51 , 52 , 53 ].

As an abundant source of human hepatocytes, iPSC-HLCs exhibit comparable phase II enzyme expression to HepG2, and a similar metabolite profile to HepaRG [ 54 , 55 ]. Furthermore, the sensitivity of HLCs to various hepatotoxic compounds and the response to AHR agonists are similar to those of PHHs [ 56 , 57 , 58 , 59 ]. As a result, these cells are currently widely used for high-throughput hepatotoxicity screening [ 60 , 61 , 62 ] and even transcriptomic compound toxicity research [ 63 , 64 ]. HLCs also serve as tools for repeated dose testing of single or combination drugs, such as rifampicin and/or cyclosporine A, amiodarone and troglitazone [ 65 , 66 ].

One major challenge that applies to all iPSC-derived cell models is to resolve the immature state of the final cell type. iPSC-derived cells often resemble fetal rather than adult cells in terms of their epigenome, gene expression profiles and phenotype [ 67 , 68 ]. To address this issue, several studies have explored strategies to significantly improve the maturity of HLCs [ 57 , 69 ]. HLCs can be transferred to 3D scaffolds permitting a more physiological arrangements and interaction of cells. The details of culturing iPSC-HLCs based on 3D scaffold structure will be explained in Sect.  Liver spheroids . The overexpression of liver-enriched transcription factors (HNF4α and HNF1α) and forkhead box (FOXa2 and FOXa3) was discovered to improve differentiation efficiency and promote iPSC-HLCs maturation [ 70 ], which was successfully used to assess drug-induced cytotoxicity [ 71 ].

Overall, iPSCs-HLCs are capable of facilitating compound optimization, precision medicine, and drug screening, but protocols for standardizing these cells still need improvement. Furthermore, the current price of commercially available iPSC-HLCs is 30-50% higher than that of PHHs [ 72 ], which hinders their widespread application.

Multicellular co-cultures

The liver is composed of various cells, including hepatocytes, which are the most abundant and mainly responsible for metabolism, biliary epithelial cells, hepatic stellate cells (HSCs) that store vitamin A, Kupffer cells (KCs) that are resident macrophages, and hepatic sinusoidal endothelial cells. They coordinately regulate liver function in multiple microenvironments [ 73 ]. DILI may be caused by interactions between different types of liver cells. Therefore, a more comprehensive model should incorporate multiple cell types. Substantial improvements in in vitro models can be achieved through developing co-cultures composed of liver parenchymal cells (PCs) and non-parenchymal cells (NPCs). Applying such strategies leads to functional properties that mimic the liver microenvironment more closely and thus the in vivo conditions [ 74 , 75 ].

Due to the release of inflammatory mediators and growth factors activated by drugs, HSCs and KCs play crucial roles in regulating DILI. Therefore, most co-culture systems for DILI involve combinations of PHHs and HSCs and/or KCs [ 76 , 77 , 78 , 79 ]. Compared to monolayer hepatocyte cultures, combined PHHs have been shown to have longer lifespans and better functionality, making them more suitable for long-term studies involving repeated drug administration while retaining responsiveness to inflammatory stimuli [ 80 , 81 ]. Bell et al. identified the optimal ratio of PHHs to NPCs as 2:1, and found supplementation of NPCs in PHH spheroids mitigated the toxicity of acetaminophen (APAP). Meanwhile, spheroids exhibited lower expression levels of several CYP450 enzymes involved in APAP bioactivation (e.g., CYP2E1, CYP3A4, and CYP1A2), as well as higher levels of miR-382 and miR-155, which have potential roles in liver regeneration and inflammation. They emphasized the potential value of adding NPCs to PHH spheroids for mechanistic studies of APAP-induced immune and steatosis responses [ 82 ]. Additionally, besides PHHs and NPCs, Nguyen et al. established a co-culture of human-derived hepatocytes and KCs, finding that compared to single cultures, the co-cultures increased the release of pro-inflammatory cytokines under IL-1β mediation, upregulated acute-phase proteins, and inhibited metabolic enzymes and transporter proteins to evaluate the indirect effects of cytokines on hepatocytes [ 75 ].

Micropatterned co-culture (MPCC) technology allows control of cell arrangement in the co-culture platform, which increases the interaction area between cells while providing high-resolution observation of cell behavior and signaling [ 83 ]. MPCC created from multiple donors reflects high sensitivity and near-perfect specificity to hepatotoxic drugs, allowing improved prediction of DILI without sacrificing specificity [ 57 , 83 ]. Currently, MPCCs generated from PHHs and mouse fibroblasts have been utilized to further investigate the relationship between the inhibitory potential of the bile salt export pump (BSEP) and the risk of DILI [ 84 ], as well as to simulate the environment of non-alcoholic fatty liver disease (NAFLD) and evaluate valproic acid (VPA)-induced hepatic steatosis [ 85 ].

In comparison to 2D monolayer co-culture, most publications prefer to implement co-culture in 3D models. Therefore, this term will be frequently referenced in the subsequent 3D culture section.

2D versus 3D cultures

2d cultures.

Conventional 2D culture system has been practiced widely for many years. The standard method involves spreading a monolayer of hepatocytes on an ECM-pretreated culture dish to allow them to adhere and diffuse. This modality is inexpensive and well-established, making it easier to observe and measure cells while maintaining critical cellular functions over a short period of time. However, hepatocytes in this system gradually undergo morphological and structural changes over time and rapidly lose function due to epithelial-mesenchymal transition.

As previously stated, the adhesion of hepatocytes between two layers of ECM to form a sandwich structure of cell-cell and cell-matrix interactions is a promising method for drug toxicity screening. Sandwich cultures are able to preserve cell viability, decelerate functional decline, stabilize basic DMETs and related gene expression [ 86 , 87 ], and enhance the secretion of organic compounds such as urea and albumin for a prolonged duration [ 88 , 89 ] compared to cells seeded on top of a collagen matrix. Recent studies have utilized more advanced biomaterials, such as artificial materials with hydrogel properties, as substitutes for collagen and Matrigel to imitate the liver microenvironment [ 90 ], or attempted to restructure or purify ECM to improve hepatocyte attachment and viability [ 91 ].

3D cultures

3D cultures enable cells to better interact with their surrounding environment compared to 2D cultures. It is believed that therefore 3D cultures more accurately represent the real cell microenvironment, simulates the structure and function of tissues, and reflects differences in drug treatment. 3D configured cells exhibit complex cell-cell, cell-matrix, and cell-nutrient interactions, as well as more physiologically relevant properties, such as integrin ligation, cell contraction, and intracellular signaling [ 92 , 93 ]. This section provides an explanation of several commonly used 3D liver culture models.

Liver spheroids

Hepatocytes are capable of self-assembling and reconstituting cell contacts to form non-adherent aggregates known as spheroids. Spheroids comprise three concentric zones: an outer zone composed of less mature or undifferentiated cells, an intermediate zone where cells transition from an undifferentiated to a differentiated state, and a central zone consisting of mature or differentiated cells. These cellular layers are defined by microenvironmental conditions and signaling molecules that vary from the periphery to the center of the spheroid. The simplest protocol for culturing spheroids involves seeding hepatocytes in ultra-low attachment vessels and centrifuging them to establish spheroids through their spontaneous self-aggregation [ 33 ]. Another option is to accumulate droplets of suspended cells into microtissues under the influence of gravity. The spheroids can be harvested for direct histological analysis or transferred to non-adherent well plates for long-term culture or drug screening [ 94 ]. The spheroids established in both cases sustain liver polarity and expression of typical markers for at least five weeks [ 95 ]. Liver spheroids are composed of one or various cell types, including immortalized hepatocytes, PHHs or iPSC-HLCs with or without NPCs. Although experimental conditions using immortalized hepatocyte spheroids might be more difficult to control. As these cells continue to proliferate, the spheroid size will increase over time, creating a hypoxic core that eventually becomes necrotic [ 96 ].

Spheroids produced through non-scaffold growth methods are typically uniform in both size and shape. In comparison to monolayer cultures, all hepatocyte spheroids exhibit consistently higher levels of albumin and urea secretion, as well as phase I and II metabolic enzyme activities and transport factor intensities [ 97 , 98 , 99 ]. HepaRG spheroids display more sensitive cytotoxicity upon repeated exposure to some hepatotoxic drugs, such as APAP, VPA and cyclophosphamide [ 100 ]. It is possible that HepG2 spheroids are less sensitive to DILI than HepaRG spheroids due to low or absent expression of several CYP enzymes [ 101 ]. However, Basharat et al. suggested that spheroids derived from the modified HepG2, HepG2 (C3A), exhibited greater sensitivity to DILI compared with HepaRG spheroids when exposed to 150 compounds [ 102 ]. PHH spheroids offer distinct advantages as a 3D in vitro model of DILI. Bell et al. developed an approach for preserving and extending the viability, transcriptomic, proteomic and metabolomic phenotypes and functions of PHH spheroids in the absence of serum and demonstrated their suitability for detecting long-term DILI [ 33 ]. Meanwhile, compared with PHHs monolayer culture, PHHs spheroids show moderate sensitivity and high specificity after treatment with hepatotoxic drugs, and long-term exposure improves the sensitivity of detecting DILI-positive drugs [ 103 , 104 ].

Scaffold-free 3D spheroids offer benefits in simulating interactions, whereas scaffold-based cultures are relatively more stable and efficient. It is possible that cells encapsulated in a material scaffold adhere and develop steadily because of the physical support provided. Scaffold materials are categorized into natural extracellular matrix (ECM) and synthetic materials with hydrogel properties, such as poly-l-lactic acid and hybrid poly-l-lactic acid/poly. Scaffold-based system appears to be more suitable for iPSCs-derived HLCs. In comparison to traditional 2D culture, 3D scaffolds expedite liver maturation of HLCs and substantially increase signature mRNA levels, irrespective of their biochemical properties [ 105 , 106 , 107 , 108 ]. Compared to synthetic materials, HLCs differentiated in 3D ECM-based scaffolds exhibit significantly higher expression of mature liver markers and CYP450 enzyme activity, as well as enhanced proliferation ability [ 108 , 109 ]. Additionally, Parvanak et al. discovered that up-regulation of miR-122 and down-regulation of off-let-7f further improve the differentiation of scaffold-based cultured HLCs without the need for exogenous growth factors. But this approach has not yet been widely adopted [ 110 ].

Liver organoids

Organoids contain a population of self-renewing stem cells that share a spatial organization similar to that of their corresponding organs and are capable of reproducing partial functions, thereby providing a relevant pathophysiological system [ 111 ]. In contrast, spheroids are usually formed by the aggregation of one or more cell types and have a relatively simple structure. Spheroids are typically simpler to construct, while organoids tend to maintain organic complexity and functional architecture [ 112 ]. Human liver organoids (HLOs) are ideal in vitro models that can capture the complex interactions between PCs and NPCs within the liver to regulate metabolic activity.

HLOs express phase I and II enzymes for up to three months at levels equivalent to PHHs and human liver [ 113 , 114 ]. Consequently, HLOs models for investigating and predicting DILI have been developed. Au et al. established organoids using HepG2 and NIH-3T3 cells and introduced the organoids for drug screening (MODS) platform to analyze pathways of cell apoptosis and necrosis induced by APAP [ 115 ]. Leite et al. observed fibrotic characteristics in HLOs derived from HepaRG cells and HSCs after repeated exposure to allylalcohol and methotrexate for two weeks, and these features include HSCs activation, collagen secretion and deposition. Additionally, based on the identification of these organoids, it was found that APAP is able to indirectly induce HSCs activation, thereby mediating hepatotoxicity. This conclusion was further validated in mice [ 116 ].

HLOs generated from iPSCs are more practical for large-scale DILI risk assessment because of their scalability. Zhang’s team demonstrated that iPSCs-HLOs from three different sources exhibit DILI predictive capabilities similar to intact HLOs in high-throughput screening, and observed clear morphological disparities in cells treated with drugs targeting different mechanisms [ 117 ]. Additionally, they established the patient-derived liver-on-chip (PaDLOC) system, validating that iPSCs-HLOs combined with PaDLOC show high physiological similarity to the human liver, enabling assessment of more complex DILI mechanisms [ 117 ]. The DILI identification method developed using iPSCs-HLOs displayed high sensitivity (88.7%) and specificity (88.9%) for 238 marketed drugs at four different concentrations [ 61 ]. Addressing the issue of limited expansion and further differentiation of iPSCs-HLOs, Mun et al. devised an efficient and reproducible strategy for generating iPSCs-HLOs [ 118 ]. Additionally, they verified the maturity of these organoids through comprehensive transcriptomic analysis and performance assays, confirming their heightened susceptibility to clinical drugs capable of inducing hepatotoxicity and steatosis [ 118 ].

Future advancements may position HLOs as a standard in vitro model for compound toxicity screening. Some drawbacks of HLOs, including limited maturity and functionality, as well as challenges in controlling heterogeneity [ 119 ], which are indeed also present in liver organoids, are expected to be gradually overcome.

3D bioprinted liver

3D bioprinting is the utilization of 3D printing technique to integrate cells, growth factors, and/or biomaterials, aiming to fabricate biomedical components. The application of this technology to study liver disease emerged only a few years ago. 3D bioprinting facilitates the spatially coordinated growth of almost all hepatocytes, encompassing HepG2 [ 120 , 121 ], HepaRG [ 122 ], PHHs [ 123 , 124 ] and iPSCs-HLCs [ 53 ], and less commonly used hESCs- HLCs [ 125 ] and iPSCs-derived liver progenitor cells (HPCs) [ 126 ]. All of them retain stable functionality and viability in long-term culture.

Liver tissues generated by 3D bioprinting have demonstrated superior sensitivity and accuracy in detecting DILI compared to 2D cultures and 3D spheroids for a selection of hepatotoxic compounds [ 53 , 122 , 123 ]. Moreover, liver bioprinting effectively replicates DILI scenarios and distinguishes highly homologous compounds, such as trovafloxacin and levofloxacin, both belonging to the quinolone class [ 124 ]. Interestingly, an emerging model named exVive3D bioprinted liver is being exploited as the first commercially available human liver tissue to evaluate drug hepatotoxicity [ 127 ]. ExVive3D liver tissue closely resembles native liver tissue with intercellular connections, microvascular structures, and cellular compartments. Damage caused by APAP insult mirrors that observed in liver specimens from patients overtreated [ 128 ]. In brief, it is possible that the remarkable biological properties of 3D bioprinted liver models improve the assessment of potential clinical DILI risk to some extent.

Liver-on-a-chip

Microfluidic culture devices are a branch of microfluidic technology, and organs-on-chips represent a specialized subtype of microfluidic chips [ 129 ]. Organs-on-chips incorporate physiologically relevant fluid shear stress and cyclic strain, analyze organ-specific responses to antigenic invasion, as well as more e precisely adjust environmental conditions based on the cultured cell types [ 130 ].

Liver-on-a-chip has emerged in recent years, resulting in limited literature on DILI. Bhise et al. encapsulated HepG2/C3A cells within hydrogels to construct liver spheroids, which were then arranged in microfluidic chips. After one month, they found that metabolic activity and four biomarkers of the chip significantly decreased over time post-APAP treatment. This suggests the potential of liver-on-a-chip as a complementary tool for assessing acute drug toxicity [ 131 ]. Zuchowska’s team cultivated HepG2 spheroids in a microfluidic system and observed their response to selected concentrations of 5-fluorouracil (5-FU), revealing the decrease in 5-FU resistance with increasing spheroid diameter [ 132 ]. Moreover, a large-scale screening of 122 clinically hepatotoxic drugs indicated that the 3D PHHs model integrated into a biomimetic chip displayed greater sensitivity to DILI compared to 2D cultures [ 133 ].

Microphysiological system (MPS) is defined as a novel microfluidic platform that reconstruct the characteristics of tissue microenvironments [ 134 ]. This sophisticated platform has the capability to enhance cell differentiation and maturation and extend enzyme function and albumin production for at least one month after isolation or inoculation [ 135 ]. Hepatic MPS successfully replicate the hepatotoxic effects of trovafloxacin across various batches. Furthermore, owing to the sustained activity of CYP3A4 and albumin secretion over an extended duration, liver cell function within MPS remains more stable [ 136 ].

Liver-on-a-chip demonstrates minimal loss of specific functions and viability, while enabling precise control of the local environment and the simulation of human organs, surpassing other 3D technologies [ 135 , 137 ]. Furthermore, compared to static models, perfusion of cultures allows for continuous nutrient exchange and oxygen delivery, which enhance and sustain liver-specific functions [ 138 ]. We look forward to more evidence from liver-on-a-chip technology to further explore DILI in the future.

Cellular responses as targets for DILI

The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have both established industry guidelines for premarketing clinical and/or nonclinical evaluation of DILI. Nonetheless, there has been limited progress in confronting the regulatory obstacles created by the adverse drug effects. Biomarkers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and bilirubin, have been utilized for over four decades as indicators of liver drug safety [ 139 ]. The insufficiency of these markers has been recognized in FDA guidelines. In the following section, the most prominent cellular mechanisms underlying DILI will be discussed (Fig. 1 ).

A reductionists view of DILI. Xenobiotic substances can trigger a range of intracellular disturbances linked to DILI risk, including ( 1 ) mitochondrial dysfunction, ( 2 ) production of reactive metabolites and oxidative stress, ( 3 ) ER stress, ( 4 ) Lysosomal impairment, and ( 5 ) inhibition of biliary efflux

• Mitochondrial dysfunction

Mitochondria are vital organelles in eukaryotic cells, primarily responsible for generating adenosine triphosphate (ATP) to meet the energy demands for cell survival. They also play an indispensable role in liver synthetic metabolism and catabolism [ 140 ]. Defects or alterations in mitochondrial activity can result in mitochondrial dysfunction (MDF), leading to the onset of various diseases.

The concept that drugs may interfere with mitochondrial function as a mechanism of hepatotoxicity originates from 1995, when clinical trials documented symptoms of mitochondrial damage in patients receiving drug therapies [ 141 ]. Since then, evidence of adverse events caused by DILI that leads to MDF has continuously expanded. In 2021, FDA released a report listing 192 compounds categorized as having the highest risk of DILI, with calculations indicating a higher proportion of drugs capable for triggering mitochondrial toxicity, resulting in severe liver injury in patients [ 142 ].

Mitochondrial energy production primarily relies on oxidative phosphorylation (OXPHOS) and the tricarboxylic acid cycle (TCA). Several drugs cause severe ATP depletion by directly harming OXPHOS and electron transport chain (ETC) in the liver, encompassing two procedures. Some non-steroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen and nimesulide, uncouple OXPHOS without inhibiting ETC activity [ 143 , 144 ], but their effects are relatively mild and may not lead to adverse consequences in vivo. Dual inhibition of OXPHOS and ETC further impair the oxidation process. For instance, amiodarone as an OXPHOS uncoupler restrains ETC and indirectly affect β-oxidation [ 145 ]. Unfortunately, the mechanism remains unclear, with evidence suggesting that this interaction appears to depend on drug concentration [ 146 , 147 , 148 ].

The mitochondrial permeability transition pore (MPTP) mediates mitochondrial permeability transition (MPT), leading to the loss of structural and functional integrity of the mitochondrial membrane [ 149 ]. Mitochondria involved in MPT release pro-apoptotic factors, including Bcl-2 family proteins and cytochrome c [ 150 ]. Some drugs trigger uncontrolled opening of MPTP, causing widespread hepatocyte apoptosis and necrosis. High doses of APAP initiate MPT by activating c-Jun N terminal protein kinase (JNK) or other inducers, resulting in irreversible MDF and cell death related to JNK activation [ 151 , 152 , 153 , 154 , 155 ]. Moreover, the JNK pathway can be activated by reactive oxygen species (ROS), leading to the translocation of phosphorylated JNK to the mitochondria, where it interacts with Sab protein, further exacerbating oxidative stress and ROS production [ 156 ]. Hepatotoxic drugs that excessively impact oxidative stress [ 157 , 158 , 159 ] and ferroptosis [ 160 ] have also been shown to interfere with MPT.

Drugs can directly impact β-oxidation by inhibiting the formation of long-chain fatty acids that enter the mitochondria, leading to the accumulation of triglycerides within hepatocytes and consequent steatosis [ 161 ]. VPA is capable of consuming free Coenzyme A (CoA) and carnitine palmitoyltransferase (CPT), disrupting β-oxidation and leading to MDF [ 156 , 162 ]. Tetracycline is believed to interfere with the oxidation process by down-regulating genes involved in β-oxidation and TCA cycle [ 163 , 164 ]. Additionally, tamoxifen, tetracycline, and fluoroquinolone broad-spectrum antibiotics are hypothesized to disrupt mitochondrial DNA (mtDNA) synthesis by interacting with mitochondrial topoisomerase [ 147 , 165 , 166 ].

The intricate mechanisms of mitochondrial toxicity render it such that distinct endpoints may regulate the same event [ 167 ], and the same drug may influence different mitochondrial functions [ 156 , 159 , 168 ]. Additionally, DILI is multifaceted, and solely assessing MDF appears to be inadequate for evaluating the risks of DILI. Therefore, it may be valuable to consider co-predictions for different drugs or targets.

Chemically reactive metabolites and oxidative stress

Chemically reactive metabolites (CRMs) arise during the drug biotransformation process and induce DILI by binding to essential macromolecules and blocking their function. DMETs serve as the primary hepatic catalysts for drug metabolism and sometimes produce elevated levels of CRMs [ 169 ]. The formation of CRMs may result in oxidative stress [ 170 ]. Oxidative stress occurs when there is insufficient antioxidant capacity to deplete the excess ROS and other organic free radicals, a condition implicated in DILI [ 171 ].

Although hepatocytes possess an antioxidant system composed of molecules such as glutathione (GSH), superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT), the metabolism of some drugs consumes this defense capacity, leading to oxidative stress before liver injury occurs [ 171 ]. In addition to the aforementioned MDF, excess intake of APAP also accelerates the synthesis of a toxic CRM named N-acetyl-p-benzoquinone imine (NAPQI). Elevated levels of NAPQI deplete GSH, resulting in defective removal of ROS and activation of various complex molecular pathways, thereby triggering liver damage and hepatocyte necrosis [ 172 ].

Antipsychotic drugs at abnormal doses also elicit oxidative stress in the liver. Rats treated with fluoxetine showed an increase in overall oxidative status and a reduction in antioxidant capacity in the liver [ 173 , 174 ], manifested by the deterioration of SOD, GPx, CAT and GSH, coupled with upregulation of pro-apoptotic factors and downregulation of anti-apoptotic factors [ 175 ]. Sertraline is able to cause MDF characterized by rapid ATP depletion and induction of MPT, as well as enhance lipid peroxidation and nitric oxide production in the liver [ 176 , 177 ]. Both chlorpromazine and clozapine have been proved to undergo N-oxidation to form highly active CRM, generate excess ROS, reduce SOD and CAT enzymatic activities and GSH-dependent defense to impair the antioxidant system ultimately [ 178 ]. Clozapine even has a higher potential than fluoxetine to induce hepatotoxicity [ 173 ].

One challenge encountered in interventions aimed at oxidative stress is the necessity of administering hepatotoxic drugs at concentrations much higher than those in patients, both in in vitro and in vivo research, to permit the accumulation of CRMs or ROS that leads to DILI toxicity. However, this problem is prevalent in preclinical investigations of DILI, and overcoming the obstacle remains an unresolved issue.

Endoplasmic reticulum stress

The endoplasmic reticulum (ER) is an organelle that plays an essential role in many cellular processes, including protein synthesis and processing, lipid synthesis and calcium storage. Hepatocytes rely heavily on the ER for producing large amounts of secretory proteins such as albumin and alpha-1-antitrypsin. Hence, hepatocytes are enriched in ER and are extremely sensitive to ER perturbation and stress. ER stress initiates an adaptive cellular response known as the unfolded protein response (UPR), which aims to restore ER homeostasis and promote cell survival. Various conditions trigger UPR activation, including alterations in redox status, glycosylation, calcium homeostasis, and the burden of secretory proteins. Accumulating evidence suggests that hepatotoxic drugs may induce ER stress, potentially contributing to DILI.

UPR signaling is mediated by three distinct ER-resident proteins that act as stress sensors: inositol-requiring protein 1 (IRE1), PKR-like ER kinase (PERK) (also known as EIF2AK3) and activate transcription factor 6 (ATF6). In non-fasted mice subjected to repeated pretreatment with VPA followed by high-dose APAP administration, mRNA levels of CCAAT-enhancer-binding protein homologous protein (CHOP), IRE1 splicing, and eIF2α phosphorylation were induced after 4 h. Furthermore, neither VPA nor APAP alone activated UPR markers compared to the control group [ 179 ]. Uzi et al. also demonstrated that oral gavage of APAP strongly stimulated the expression of CHOP, Xba1 splicing, and eIF2α phosphorylation [ 180 ]. Li et al. found that SPHK1 levels in the liver were significantly elevated following APAP treatment. The phosphorylation of SPHK1 activates the PERK-eIF2α-ATF4 pathway and ATF6 (activating transcription factor 6), induces the production of CHOP, and ultimately promotes ER stress [ 153 ]. We hypothesized that high-dose APAP promotes ER stress, with the apoptotic factor CHOP playing a crucial role in APAP-induced ER stress during DILI.

In addition to APAP, trovafloxacin and efavirenz induce hepatocyte injury by triggering ER stress and UPR at clinically relevant concentrations [ 181 , 182 ]. However, it is worth noting that ER stress or UPR may be late events in DILI, or even secondary consequences of other adverse events such as MDF and oxidative stress [ 180 , 183 ]. In conclusion, further investigation is needed to determine whether ER stress is a primary mechanism driving DILI.

Lysosomal dysfunction

Lysosomes contain over 60 different hydrolases, and defects in lysosomal degradation or transport can lead to lysosomal dysfunction [ 184 ]. While lysosomes function as degradation centers in various cells, there is a notable lack of articles on lysosomal dysfunction and DILI. Excessive APAP exposure cause lysosomes to become destabilized and release cathepsin B into the cytoplasm, but it does not instigate liver damage [ 185 ], although this lysosomal protease was previously identified for contributing significantly to liver injury [ 186 ]. The lysosome is the major source of iron, and the destruction of lysosomes by APAP may lead to the release of Fe²⁺ into the cytoplasm, ultimately resulting in hepatocyte death [ 151 ]. Diclofenac has been noted to regulate hepatotoxicity by attenuating cathepsin activity and inhibiting autophagic flux [ 187 ].

Lysosomal dysfunction is not necessarily a definitive indicator to prevent drug development. Nevertheless, Uzhytchak’s review enumerated certain nanodrugs that could cause adverse effects through lysosomal dysregulation [ 188 ], expecting the possibility of further advancements in the field of nanomedicine.

Alterations of the bile salt export pump

Drug-induced cholestatic liver injury accounts for the majority of DILI reports. The secretion of bile acids (BAs) is related to the function of the hepatobiliary transporter system. When the homeostasis of BAs is destroyed, the concentration of BAs will elevate abnormally and become hypertoxic to hepatocytes. Bile salt export pump (BSEP) is primarily responsible for excreting BAs into bile through an ATP-dependent mechanism.

Impaired functioning of liver transporters is a key pathogenic factor in cholestasis, with BSEP being the most significant among these transporters. Impaired BSEP caused by defective gene regulation may contribute to hereditary or acquired cholestatic diseases [ 189 ]. Several drugs have been identified as BSEP inhibitors. Ogimura et al. employed sandwich-cultured rat liver cells to screen 26 hepatotoxic compounds, of which 11 exhibited BA-dependent drug toxicity, with 9 of them being verified as BSEP inhibitors [ 190 ]. Garzel et al. also utilized sandwich-cultured PHHs to assess 30 BSEP inhibitors, demonstrating the significant role of BSEP inhibition in drug-induced cholestatic hepatotoxicity [ 191 ]. Moreover, they suggested that the transcriptional suppression of BSEP by lopinavir and troglitazone might result from their interaction with the farnesoid X receptor (FXR) [ 191 ]. Interestingly, in human hepatocytes, troglitazone and its metabolites competitively inhibited BSEP leading to hepatotoxicity [ 191 , 192 ], whereas this phenomenon was not observed using rat hepatocytes [ 193 , 194 ].

Sulindac and bosentan, both withdrawn from the market due to acute-specific DILI, have also been found to suppress BSEP and accumulate intracellular bile salt-independent bile flux [ 195 , 196 , 197 ]. Additionally, Guo et al. detected that isoniazid and rifampicin induced DILI through downregulating BSEP, resulting in bile acids accumulation in mice [ 198 ].

An interesting observation is that in vitro models appear to be more sensitive than in vivo models in testing the response to BSEP transcription. This difference could be attributed to variances in bile salt composition and compensatory mechanisms present in rodents [ 193 , 194 , 196 ]. Monitoring alterations in BSEP activity are able to provide insights into the occurrence, delayed manifestations, and species variations of DILI, assisting in the evaluation of the hepatotoxic potential of candidate drugs. However, there are also opposing views. For instance, Chan et al. argued against the notion that BSEP alone or in combination with other liver transporters can accurately predict DILI [ 199 ]. Therefore, while BSEP is considered a valuable factor in drug-induced cholestatic hepatotoxicity, its impact on other types of DILI requires further verification.

Omics technologies for investigating DILI

“Omics” is a broad term referring to research fields in the biological sciences that end with “omics”, such as genomics, transcriptomics, proteomics, and metabolomics. The introduction of omics technologies has tremendously accelerated progress in personalized medicine and pharmacological research. It enables the generation of large, complex datasets describing cellular biology at the molecular level. “Omics” approaches can be used for identification or classification of toxic compounds or to generate mechanistic understanding of cellular processes underlying adverse outcomes to those compounds. Therefore, “omics” technologies are very suited to enhance preclinical safety assessment of DILI [ 200 ]. In this chapter, we will enumerate the pros and cons of prevalent omics technologies related to DILI.

Genomics, one of the earliest developed omics, focuses on assembling and analyzing the function and structure of entire genome using high-throughput DNA sequencing and bioinformatics [ 201 ]. Single nucleotide polymorphisms (SNPs), which involve substitutions of single nucleotides at specific locations in the genome, contribute to the interpretation of susceptibility to xenobiotics.

The susceptibility to DILI primarily originates from SNPs in immune-related genes, such as the human leukocyte antigen (HLA) or cytokine-related genes. HLA alleles have been identified as significant susceptibility factors for several DILI drugs through genome-wide association studies (GWAS), with strong evidence found in drugs like amoxicillin-clavulanate (AC) [ 202 , 203 , 204 , 205 ], flucloxacillin [ 206 , 207 ], and lapatinib [ 208 , 209 , 210 , 211 , 212 ]. Moreover, isoniazid, minocycline, terbinafine and nevirapine have also been linked to high-risk HLA haplotypes [ 213 , 214 , 215 , 216 ]. Cytokines comprise interleukin (IL)-4, IL-10, and tumor necrosis factor (TNF)-α. Patients with diclofenac-hepatotoxicity exhibited a higher frequency of variant alleles IL-10-A627C and IL-4-T590C, with the highest odds ratio observed when both were present simultaneously [ 217 ]. Furthermore, TNF-α-G308A has been found to be significantly associated with hepatitis induced by isoniazid and rifampicin [ 218 ].

In addition to immune-related SNPs, genetic variants that lead to decreased activity of certain phase II liver enzymes appear to influence the occurrence of DILI, with N-acetyltransferase 2 (NAT2) being the most typical. Patients with NAT2 mutations displayed reduced substrate acetylation and remarkably enhanced sensitivity to liver injury mediated by isoniazid and rifampicin, both of which are NAT2 substrates [ 219 ]. Chan et al. also demonstrated a correlation between isoniazid-DILI and slow acetylation variants of NAT2 [ 220 ]. Furthermore, individuals carrying double T1-M1 null genotype of glutathione-S-transferase (GST) are at an elevated risk for DILI caused by troglitazone [ 221 ], tacrine [ 222 ], NSAIDs and AC [ 223 ].

However, predicting the ultimate biological action solely through genomic analysis is challenging due to epigenetic, transcriptional, and post-translational DNA transformations. Therefore, genomics could serve as a baseline for providing comprehensive DNA information.

Transcriptomics

Transcriptomics, the subsequent layer of genomics, primarily focuses on measuring gene expression levels through quantifying mRNA, and other non-coding RNAs. There are two key contemporary technologies in this field: microarrays and RNA sequencing (RNA-seq). The main difference between them is that the former depends on the hybridization of pre-designed labeled probes with target sequences, while the latter allows for direct sequencing of the entire transcriptome [ 224 ]. RNA-seq is presently recommended for transcriptional profiling research because of its broader coverage, absence of species limitations, and greater flexibility [ 225 ]. Single-cell RNA-seq can even report the transcriptome of human hepatocytes and even the hepatic immune microenvironment [ 226 ].

Transcriptomics-driven toxicogenomics has been advocated for precise determination of dose-dependent hepatotoxicity [ 227 ]. Based on the concept of big data compression and fusion, it has also been applied to evaluate diverse DILI cell models and identify the operational mechanisms of specific hepatotoxic drugs [ 228 ]. Wolters et al. conducted multi-omics analyses and discovered 6 persistent microRNAs associated with drug-induced cholestatic pathway under repeated doses of cyclosporine A [ 229 ]. Two years later, they integrated cross-omics perturbation data to expose new insights into mitochondrial-nuclear crosstalk, supporting for the dynamic induction of MDF, steatosis and hepatotoxicity by VPA [ 230 ].

Moreover, transcriptomics can also be utilized for screening potential biomarkers for DILI. Transcriptome differential analysis revealed that non-lethal doses of APAP upregulated the expression of Myc, Bag3 and Btc in mouse liver, whereas lethal doses did not, indicating these genes may play crucial roles in the adaptive protective response to DILI [ 231 ]. Ward et al. pointed out significant elevation in the expression of hundreds of microRNAs in plasma or serum after APAP administration, preceding ALP, particularly miR-122 and miR-192 [ 232 , 233 ]. Meanwhile, there was evidence suggesting miR-122, which exhibits liver specificity, could serve as an emerging DILI biomarker [ 234 ].

Transcriptomics is more appropriate as an intermediate step, because post-translational modifications impact the protein expression, and facilitate the identification of alterations in genetic expression profile following toxicological drug treatments.

Proteomics typically involves protein purification and mass spectrometry. It constitutes a crucial follow-up to genomics and transcriptomics in biological systems. Proteomics is inherently more intricate than genomics because the genome of an organism remains largely constant, whereas its proteome varies across cell types and over time. Moreover, the majority of proteins undergo extensive post-translational modifications [ 235 ].

Proteomics has been the standard method for measuring elevated liver enzymes in the blood of patients, but they are only detected after injury has occurred, and some drugs increase plasma liver enzymes without causing actual liver damage. By testing ALT, AST and ALP binding to apolipoprotein E, α-trypsin inter-inhibitor, serum amyloid P-component, gelsolin and complement component C7, a diagnostic accuracy of 95% for DILI can be achieved [ 236 ]. Proteomics is able to screen out toxin-reactive proteins superior to ALT and AST in patients with APAP or carbon tetrachloride overdose [ 237 ]. Furthermore, proteomics quantified alcohol dehydrogenase 1B, which rose extremely in serum samples of APAP-poisoned patients, and subsequently decreased to minimal levels during recovery [ 238 ]. It also quantified calmodulin in mouse urine to identify APAP-DILI [ 239 ], an approach that was both non-invasive and preceded ALT increase. Patients were considered as positive for diclofenac-induced DILI when the integrin beta 3 expression in whole blood was below the determined threshold of 60%, and the evaluation of this protein was as simple as liver enzymes and focused specifically on diclofenac [ 240 ].

It is also possible that proteomics clarifies mechanisms of hepatotoxicity. In the case of the widely known issue of APAP overdose, it offered valuable insight into the necrosis and self-repair profiles of liver proteins [ 241 ], and identified the activation of protective signaling pathway against hepatotoxicity [ 242 ]. Long-term low-dose exposure to bisphenol A altered the expression of phosphorylated proteins involved in metabolic and antioxidant defense in the liver [ 243 ]. Paemanee et al. found that nearly 40% of differential regulatory proteins after nevirapine treatment were mitochondrial proteins, indicating this drug may mediate mitochondrial dysregulation in liver cells [ 244 ].

The application of proteomics in drug toxicology holds promising potential, although it may not be as sensitive to individual proteins as immunohistochemical methods like Western blotting or ELISA. Indeed, the techniques for protein isolation and measurement are quite complex and multi-faceted due to the unique properties exhibited by each protein, such as mass, isoelectric point, solubility and stability. Therefore, the design of experiments and interpretation of proteomic data requires a case-by-case strategy.

Metabolomics

Metabolomics aims to measure, identify and quantify a wide range of small molecules to understand metabolic pathways, biochemical reactions, and the impact of external factors on metabolism. Metabolomics relies on non-invasive nuclear magnetic resonance (NMR) spectroscopy and invasive mass spectrometry and chromatography. Mass spectrometry stands out as a prominent method for most prospective studies as it can efficiently separate and identify individual components, with high sensitivity and suitability for high-throughput screening. The metabolic processes in human body are quite complex, leading to a wide variety of metabolites. There is currently no consensus on which single or combined metabolites are recommended for regular diagnosis and prognosis of DILI.

BAs are synthesized in the liver and accumulate in the blood during liver injury. Consequently, some studies have demonstrated the use of various bile acids in detecting DILI. Glycochenodeoxycholic acid, taurodeoxycholic acid, taurocholic acid and glycocholic acid are universally accepted as biomarkers [ 245 , 246 , 247 , 248 ]. They are able to distinguish patients with severe and non-severe DILI [ 246 , 249 ], or be combine with glycerophospholipids to determine the three phenotypes of DILI [ 250 ]. Moreover, they are also suitable for accurate classification of DILI in children [ 251 , 252 ]. In addition to the four common BAs mentioned above, glycodeoxycholic acid and obeticholic acid have also been implicated as potential biomarkers for DILI [ 252 , 253 ]. Indeed, BAs appear to be more appropriate for diagnosing cholestatic liver injury [ 254 ], because as previously explained, the secretion of BAs is a key aspect of the hepatobiliary transporter system.

Liver injury and dysfunction disrupt protein synthesis, leading to elevated concentrations of free amino acids in the liver and blood, with changes in amino acid doses potentially varying according to the severity of liver injury [ 255 ]. Chen et al. identified characteristic metabolic fingerprints of chronic DILI-associated liver fibrosis through metabolic profiling. The elevated levels of metabolites such as phenylalanine, tyrosine, tryptophan, arginine, and proline were mainly concentrated in these metabolic fingerprints [ 247 ]. Co-elevation of glutamate, alanine, leucine and phenylalanine in the serum may serve as a unique indication for VPA-DILI [ 256 ]. Moreover, first-line antituberculosis drugs impact the metabolic pathways of arginine and proline [ 257 ]. Varying opinions on the candidate amino acids for APAP-DILI may stem from age restrictions and geographical differences [ 258 , 259 ] in clinical trials. Notably, the timing of APAP treatment appears to be the sole factor associated with significant increase in ornithine [ 260 ].

Lactate participates in multiple metabolic processes within the human body, and the liver is one of the important lactate-metabolizing organs. Impaired liver function has the potential to influence lactate clearance, and excessive glycolysis also cause the hepatocytes to release lactate. The concept of blood lactate as an early predictor of the outcome in APAP-induced acute liver failure was initially suggested in 2002 by Bernal et al., who speculated that APAP had a direct toxicity on cellular respiration [ 261 ]. Therefore, elevated lactate levels usually reflect the severity of APAP-DILI in pharmacometabolomics [ 258 , 259 , 262 ], accompanied by obvious dysregulation of OXPHOS or mitochondrial function [ 263 ].

Furthermore, there are possible auxiliary metabolic biomarkers for DILI. Uric acid [ 246 , 256 , 257 ] and hippurate [ 258 , 259 ], which are the final products of purine metabolism, often emerge in urinary metabolomics. Phospholipids, crucial components of cell membranes, can be perturbed by drug-triggered events such as MDF or oxidative stress [ 247 , 256 , 264 ].

Toxicometabolomics provides a non-invasive method for sample collection, with urine and plasma endogenous metabolites proving to be more sensitive than serum biochemical parameters [ 259 ]. Furthermore, it complements a comprehensive understanding on biological systems. In summary, the effective screening of individual-specific DILI biomarkers and gaining insights into the interactions between drugs and biological processes can be achieved by analyzing multiple types of biomolecules through a multi-omics strategy.

Functional genomics by perturbation

Gene modulation techniques, such as CRISPR/Cas9, RNA interference (RNAi), and antisense oligonucleotides, have become powerful tools for generating mechanistic insights into DILI. These technologies enable precise manipulation of gene expression, allowing researchers to study the functional roles of specific genes involved in DILI mechanisms. These approaches complement traditional “omics” studies by enabling experimental validation of cause-effect relationships, providing deeper mechanistic insights into the cellular processes affected by toxic compounds. They also allow for the investigation of genetic susceptibility factors that may predispose individuals to DILI, making them particularly valuable for advancing personalized medicine and improving preclinical safety assessments. In this section, we will explore the advantages and limitations of widely used gene modulation techniques and their applications in DILI research.

Gene knockdown and knockout are two essential techniques for studying causal events in regulatory networks. Gene knockdown typically involves introducing siRNA, shRNA or other RNA interference tools to influence the translation or stability of target mRNA without entirely eliminating its function. Gene knockout, on the other hand, involves implementing gene-editing technology to completely abolish the function of the target gene, and CRISPR-Cas9 system is the most commonly used method. These fundamental molecular biology tools play an important role in validating potential cellular mechanisms related to DILI that have been identified using “omic” technologies.

Small interfering RNA (siRNA) is a short double-stranded RNA molecule capable of causing degradation or translation inhibition of mRNA by complementary pairing with the mRNA of a target gene [ 265 ]. It is possible to achieve short-term temporary gene silencing, with its influence gradually diminishing over time.

Although the application of siRNA for acute gene silencing to create liver disease models began in the early 2000s, the field of DILI has only gained momentum in animal and cell cultures in the last decade. In mice, siRNA targeting specific genes has been used to explore the protection of signaling pathways, particularly JNK, on ibuprofen-induced acute liver injury [ 266 ], as well as to assess the role of cyclin M4, which is involved in magnesium homeostasis in the ER during APAP hepatotoxicity [ 267 ]. Furthermore, it is also possible to reveal the mechanism of BAs and leukotrienes in APAP-DILI based on silenced G protein-coupled receptor [ 268 ].

In DILI studies, Nrf2-deficient siRNAs are frequently used in in vitro models. Kale et al. used siRNA to silence Nrf2 in human liver microsomes and found Nrf2 knockdown did not lead to elevated serum ALT activity or any noticeable histopathological indications of liver damage upon exposure to nimesulide. Their findings indicate that nimesulide is metabolically activated by CYP2C into protein-reactive electrophilic intermediates, which can activate the Nrf2 pathway even at non-toxic exposure levels [ 269 ]. Downregulation of NRF2 in PHHs and HepG2 cells was regarded as tool to assess the toxic potential of positive NRF2 signaling [ 270 ], characterize the impact of diclofenac and omeprazole on the Nrf2 pathway [ 271 ], and elucidate Nrf2-mediated adaptive responses to mitigate rifampicin-induced DILI [ 272 ]. This is possibly due to Nrf2, a transcription factor, playing a crucial role in regulating the intracellular antioxidant defense system and drug detoxification pathways [ 273 ]. In addition, siRNA-mediated interference in HepG2 and L02 cells provided a clear understanding of the mechanisms underlying the diclofenac- and TNF-α-induced apoptotic crosstalk [ 274 ], as well as the alleviation of cell damage in hepatocytes [ 275 ].

Short hairpin RNA(shRNA) is a synthetic small RNA molecule, similar in principle to siRNA, which is typically utilized in gene expression vectors to achieve stable and sustained gene silencing within cells. Although siRNA and shRNA perform comparable roles, they are distinct molecules with potentially different mechanisms of action and RNA interference pathways [ 276 ].

shRNA technology has been integrated into several liver injury models, including those for hepatitis B virus infection [ 277 ], cholestatic liver disease [ 278 ], and alcoholic liver disease [ 279 ]. Rats carrying shRNA against γ-glutamylcysteine synthetase were utilized in acute or subacute toxicity trials involving diclofenac and flutamide [ 280 ]. Additionally, mice with suppressed Grsf1 expression in the oxidative stress pathway were employed to determine the potential mechanisms underlying DILI caused by anti-tuberculosis drugs [ 281 ].

Although instances of shRNA serving as a tool to mimic DILI are scarce, extensive research has regarded shRNA as a lentiviral vector to silence specific genes in liver cell models, particularly PHHs. Amet et al. downregulated BST-2 in PHHs using shRNA and found a significant reduction in IFN-mediated antiviral activity against hepatitis C virus (HCV), indicating upregulation of BST-2 directly participates in IFN-mediated inhibition of HCV production [ 282 ]. Zhang’s team also knocked down GP73 in PHHs, demonstrating that GP73 acts as a negative regulator of innate immunity by promoting HCV infection through accelerating degradation of MAVS/TRAF6 [ 283 ]. Furthermore, shRNA lentivirus-mediated silencing of PXR significantly inhibited the upregulation of CYP3A4 mRNA, providing evidence that the expression of CYP2E1, CYP1A2, and AhR is regulated by the WNT/β-catenin pathway [ 284 ]. Caiment et al. also applied shRNA lentivirus to downregulate the expression of C/EBPα in PHHs, determining that prolonged exposure to VPA (24 ~ 72 h) remarkably reduces the basal and maximal cellular oxygen consumption rates, while C/EBPα, as one of the transcription factors, partially rescues VPA-induced MDF [ 285 ].

Since shRNA lentiviral transduction seemingly has no impact on cell viability or liver phenotype, it emerges as an easy-to-use method for long-term or stable downregulation of nuclear receptors and other genes in various cell models, including primary hepatocytes.

CRISPR-Cas9

CRISPR-Cas9 technology operates by guiding RNA molecules to locate and directly cut or modify the DNA sequence. This enables gene regulation through gene knockout as well as genome editing, including gene insertion or base substitution. Genome editing of hepatocytes is extensively employed to model or intervene in various human liver diseases, encompassing NAFLD, cirrhosis, liver tumors, and other forms of liver injury [ 286 ].

The genome-wide analysis is conducted using CRISPR-Cas9 to screen regulatory genes that are either protective or susceptible to APAP-DILI [ 287 ]. Additionally, the effect of rifampicin on demethylated and hypermethylated hepatocytes is investigated to explore the relationship between site-specific DNA modifications and DILI [ 288 ]. Zhang et al. also found that BACH1 played a central role in aflatoxin B1-induced oxidative damage in hepatocytes by regulating the expression of antioxidant genes based on Genome-Scale CRISPR knockout screening [ 289 ]. Interestingly, genetically modified iPSCs edited with CRISPR-Cas9 appear to be well-suited for establishing large-scale DILI screening platforms due to their multiple readouts, high prediction accuracy, and indefinite cell supply. These advantages have been demonstrated for iPSC-HLCs and iPSC-derived HLOs [ 59 , 61 ].

Compared to gene editing and insertion, the CRISPR-Cas9 system stands out as one of the most mature and efficient methods for gene knockout, so it has also been used to discover the mechanisms of liver toxicity in recent years. Lv et al. suggested that corilagin, a traditional medical drug, attenuated APAP-DILI by enhancing the AMPK/GSK3β-Nrf2 signaling pathway, as evidenced by the knockout of Nrf2 in HepG2 and mice [ 290 ]. In the latest study, mice transfected with CRISPR-Cas9-mediated Notch1 knockout or STING activation vectors were employed to highlight the critical role of the macrophage PTEN-NICD/NRF2-STING axis in regulating innate immune responses to APAP-DILI [ 291 ].

In summary, it is expected that these three technologies create effective platforms for preclinical drug testing and provide personalized medical support, with the potential to improve drug safety.

Future perspectives

This review outlines promising in vitro models, cellular responses that may serve as biomarkers, common omics technologies related to DILI, and frequently used molecular technology tools to assess gene function. However, the complexity of its origin and multifactorial nature of DILI, as repeatedly emphasized in this review, currently render the identification of a universal test object for DILI impossible.

The risk of DILI arises from the interplay of both drug and host factors, rather than from either one individually. The diverse metabolism, distribution, and excretion of different drugs in the body vary depending on the activity and rate of different uptake and metabolism pathways, which are influenced by the structure and pharmacokinetic properties of drugs. Drug factors, particularly the concentration of reference drugs, are crucial considerations. However, drug development requires comprehensive consideration of multiple factors, including drug metabolism pathways, pharmacokinetic properties, drug distribution in the body, and toxicity mechanisms. These challenges make it difficult to establish concentration standards for hepatotoxic drugs. Cmax (maximum plasma concentration) seems relatively reasonable, but it overlooks factors like drug accumulation in the liver, protein binding, and the impact of Tmax. Incorporating parameters such as area under the curve (AUC) and half-life offer more comprehensive pharmacokinetic information. Nonetheless, the unification of drug resources, compendiums, and reports remains the most crucial aspect. The Prospective European Drug-Induced Liver Injury Network (PRO-EURO-DILI-NET) Cost Action ( Action CA17112 - COST ) is dedicated to this topic. The alliance was initiated in 2018, with a focus on addressing and resolving advanced developments in preclinical human-relevant models and clinical DILI [ 292 ]. Its consensus statement provides detailed guidelines on the requirements for human-based systems to assess hepatotoxicity and to guide future drug safety testing [ 293 ].

Regarding host factors, the application of patient-derived cells in DILI modeling seems beneficial in uncovering the influence of individual risk on pathogenesis. The advent of iPSCs enables the development of patient-specific hepatocytes, serving as a host-dependent assay to investigate drug-individual interactions. iPSC-HLCs stand out as a superior alternative to immortalized liver cell lines, positioned as the optimal choice after PHHs (refer to Sect.  Hepatocyte-like cells derived from induced pluripotent stem cells ). They demonstrate high suitability for personalized cell therapy and screening of candidate pharmacological agents. Addressing concerns regarding their applicability for studying drugs requiring metabolic activation, such as APAP, co-cultures of donor-matched iPSC-differentiated KCs and iPSC-HLCs have been found to possess minimal nonspecific background reactions, effectively detecting inflammation-related drug-induced hepatotoxicity [ 49 ]. Moreover, as stated in Sect.  Liver organoids , HLOs derived from iPSCs offer tremendous value for performing high-throughput analysis of DILI risk, which is conducive for advancing personalized medicine for DILI.

A novel aspect of DILI research not discussed in this review is the relationship between DILI and the gut microbiota. Gut microbiota can influence the bioavailability, bioactivity, and toxicity of drugs through enzymatic reactions [ 294 ]. Additionally, they regulate the expression of host genes involved in metabolic pathways, including nuclear receptor signaling, phase I and II enzymes, and transport proteins [ 295 ]. As part of the gut-liver axis, disruptions in gut microbiota activity are believed to contribute to the development of DILI [ 296 ]. There seems significant potential for using the gut microbiome as a biomarker to predict the likelihood of developing DILI, as specific microbial signatures may indicate susceptibility to liver toxicity from certain drugs. Overall, the microbiome-DILI connection represents a promising and rapidly evolving area of research, with the potential to improve personalized medicine by predicting DILI risk and optimizing drug safety.

Overall, we believe the ideal system for predicting DILI should consider both the structures and properties of drugs, as well as personalized characteristics of patients, and in silico algorithms can be incorporated to improve accuracy and efficiency. The polygenic risk score (PRS) evaluates an individual’s genetic predisposition to specific traits or diseases, especially in complex conditions influenced by both polygenic and environmental factors [ 297 ]. PRS holds great significance for assessing disease risk and promoting precision medicine. Koido et al. pioneered PRS for multi-drug and multi-donor DILI, which has inspired researchers to link in silico protocols with in vitro genomics and transcriptomics. This integration aims to design more specific, efficient, and robust preclinical trials [ 298 ]. The combination of two is expected to be the next breakthrough in DILI. Furthermore, the ideal framework for predicting DILI should be based on drug-induced mechanisms that play a causal role and have been validated using adequate genetic perturbation experiments in relevant human cell models.

Data availability

This review article does not involve the generation or use of new datasets. All information presented is based onpreviously published studies and publicly available data sources.

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Guo, K., van den Beucken, T. Advances in drug-induced liver injury research: in vitro models, mechanisms, omics and gene modulation techniques. Cell Biosci 14 , 134 (2024). https://doi.org/10.1186/s13578-024-01317-2

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Received : 31 July 2024

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DOI : https://doi.org/10.1186/s13578-024-01317-2

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