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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: July 23, 2024

Published: February 22, 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: The Complete Guide for Marketers in 2024

• May 12, 2024
• by Steven Austin

Introduction

In the fast-paced, data-driven world of marketing, understanding the true impact of your strategies and campaigns is essential for success. That‘s where causal research comes in. By investigating the cause-and-effect relationships between variables, causal research empowers marketers to make informed decisions, optimize their approaches, and drive better business outcomes.

As we navigate the challenges and opportunities of 2024, causal research has become an indispensable tool in the marketer‘s arsenal. With advancements in data collection, analysis tools, and machine learning, we now have unprecedented ability to uncover complex causal relationships and derive actionable insights.

In this comprehensive guide, we‘ll dive deep into the world of causal research, exploring its fundamental concepts, benefits, processes, and real-world applications. Whether you‘re a seasoned marketer or just starting out, this article will equip you with the knowledge and strategies you need to leverage causal research for competitive advantage and marketing success in 2024 and beyond.

What is Causal Research?

At its core, causal research is a type of study that examines the cause-and-effect relationships between two or more variables. It seeks to establish whether changes in one variable (the independent variable) directly cause changes in another variable (the dependent variable), while controlling for potential confounding factors.

The key elements of causal research include:

• Independent variable: The presumed cause, manipulated by the researcher
• Dependent variable: The presumed effect, measured by the researcher
• Confounding variables: Extraneous factors that could influence the relationship between the independent and dependent variables
• Control: Techniques used to isolate the effect of the independent variable on the dependent variable, such as randomization and holding other variables constant

By carefully designing experiments that manipulate the independent variable and control for confounding factors, causal research allows marketers to establish probable causality rather than mere correlation. This is critical for making confident, data-driven decisions and avoiding the pitfalls of misleading or spurious relationships.

Why Causal Research Matters for Marketers

In today‘s intensely competitive and data-saturated marketing landscape, causal research plays a vital role in driving effective strategies and campaigns. According to a recent survey by the Marketing Science Institute, 78% of marketing leaders consider causal research a top priority for their organizations (MSI, 2023).

The benefits of causal research for marketers are numerous:

Optimize marketing mix: By understanding the causal impact of different marketing channels, messages, and tactics on customer behavior and business outcomes, marketers can optimize their resource allocation and media mix for maximum ROI.

Enhance customer targeting: Causal research helps identify the key drivers of customer acquisition, retention, and loyalty, enabling marketers to develop more precise and effective targeting strategies for different segments.

Improve product development: By testing the causal effects of product features, pricing, and positioning on customer preferences and purchase intent, marketers can inform product development decisions and create more compelling value propositions.

Boost campaign performance: Causal research allows marketers to fine-tune their creative assets, ad placements, and bidding strategies based on rigorous, data-driven insights, leading to higher engagement, conversion rates, and ROAS.

For example, a leading e-commerce brand used causal research to optimize its email marketing campaigns. By conducting A/B tests on subject lines, send times, and content variations, they discovered that personalized subject lines featuring the customer‘s name increased open rates by 29% and conversion rates by 17%. Applying these causal insights across their email program led to a 14% lift in revenue attributed to the channel (CMO Council, 2024).

The Causal Research Process

To conduct effective causal research, marketers should follow a rigorous, step-by-step process:

Define the research question and hypothesis: Clearly articulate the causal relationship you want to investigate and state your predicted outcome.

Identify variables and confounding factors: Specify the independent variable, dependent variable, and potential confounding variables that could affect the relationship.

Design the experiment: Develop a well-controlled study that manipulates the independent variable while holding other factors constant. Use randomization to assign subjects to treatment and control groups.

Collect data: Gather relevant data through surveys, experiments, observations, or secondary sources, ensuring data quality and representativeness.

Analyze results: Use appropriate statistical techniques (e.g., regression analysis, hypothesis testing) to assess the significance and magnitude of the causal effect.

Draw conclusions and apply insights: Interpret the findings in the context of your research question, considering limitations and alternative explanations. Translate insights into actionable marketing strategies and tactics.

Best practices for causal research include:

• Ensuring adequate sample size and representative sampling
• Using valid and reliable measurement instruments
• Controlling for potential confounding variables through randomization, matching, or statistical adjustment
• Conducting manipulation checks to verify the effectiveness of experimental treatments
• Replicating findings across multiple studies or contexts to establish robustness and generalizability

Conducting Causal Research: Methods and Tools

Marketers can employ various methods and tools to conduct causal research, each with its own strengths and limitations:

Randomized controlled trials (RCTs): The gold standard for causal inference, RCTs randomly assign subjects to treatment and control groups, enabling unbiased estimates of causal effects. While powerful, RCTs can be costly and time-consuming.

Quasi-experiments: These studies leverage naturally occurring variations in the independent variable to estimate causal effects, such as comparing outcomes before and after a policy change or across different geographic regions. Quasi-experiments are more feasible than RCTs but may have lower internal validity.

Observational studies: Researchers collect data on the independent and dependent variables without manipulating them directly, using techniques like regression analysis to control for confounding factors. Observational studies are more prone to bias than experiments but can provide valuable insights when experiments are not possible.

Machine learning: Advanced algorithms like causal forests, Bayesian networks, and deep learning can automate and scale causal inference, enabling marketers to analyze vast amounts of data and uncover complex causal relationships more efficiently.

Emerging tools and technologies for causal research include:

Self-service experimentation platforms: These tools allow marketers to easily design, run, and analyze experiments across digital channels, such as website testing, email campaigns, and mobile apps.

Automated insights generation: AI-powered systems can automatically detect causal relationships, anomalies, and opportunities in marketing data, providing actionable recommendations for optimization.

Real-time experimentation: Streaming data platforms enable marketers to conduct causal experiments in real-time, quickly testing and iterating on strategies based on live customer interactions and feedback.

Analyzing and Interpreting Causal Research Data

Once data is collected, marketers must analyze and interpret the results to draw valid causal conclusions. Key statistical techniques for causal analysis include:

Regression analysis: Assesses the relationship between the independent and dependent variables while controlling for confounding factors.

Hypothesis testing: Evaluates whether the observed causal effect is statistically significant or due to chance.

Effect size estimation: Quantifies the magnitude and practical importance of the causal effect.

When interpreting causal research findings, marketers should consider:

Internal validity: The extent to which the study design allows for confident causal conclusions, free from confounding factors or alternative explanations.

External validity: The generalizability of the findings to other contexts, populations, or time periods.

Limitations: Potential sources of bias, measurement error, or other limitations that could affect the validity of the conclusions.

Practical significance: The real-world impact and actionability of the findings for marketing decision-making and strategy.

Applying Causal Research Insights in Marketing

The ultimate goal of causal research is to derive actionable insights that can improve marketing performance and drive business growth. To effectively apply causal research findings, marketers should:

Translate insights into tactics: Develop specific, measurable actions based on the causal relationships identified, such as adjusting ad targeting, optimizing landing pages, or personalizing content.

Integrate across functions: Share causal research insights with cross-functional teams (e.g., product, sales, customer service) to align strategies and create seamless customer experiences.

Measure impact and ROI: Establish clear metrics and KPIs to track the performance of causal research-driven initiatives, quantifying their contribution to marketing and business outcomes.

Continuously test and learn: Embrace a culture of experimentation, using causal research as an ongoing process to test hypotheses, refine strategies, and adapt to changing customer needs and market conditions.

Case Studies and Examples

To illustrate the power of causal research in action, let‘s look at two real-world success stories:

Airbnb: The travel platform used causal research to optimize its referral program. By conducting experiments on referral incentives, email copy, and user onboarding, they discovered that offering a $25 travel credit to both the referrer and the referee increased signups by 60% and bookings by 42%. Scaling these insights globally led to a 300% increase in referral revenue (Airbnb, 2023).

Domino‘s Pizza: The restaurant chain leveraged causal research to improve its mobile app experience. Through A/B testing of app features, user flows, and promotional offers, they found that simplifying the ordering process and personalizing recommendations based on past purchases boosted conversion rates by 32% and average order value by 18%. These causal insights informed a redesign of the app, resulting in a 24% increase in mobile sales (MMA, 2024).

Future of Causal Research in Marketing

As we look ahead to the future of marketing, causal research will play an increasingly critical role in driving innovation, personalization, and customer-centricity. Key trends and predictions for causal research in 2024 and beyond include:

Automated causal discovery: Advances in machine learning will enable more efficient and scalable detection of causal relationships in large, complex datasets, reducing manual effort and accelerating insight generation.

Real-time experimentation: The proliferation of streaming data and agile marketing technologies will allow for near-instantaneous causal testing and optimization, enabling marketers to respond to customer needs and market dynamics in real-time.

Causal-driven personalization: Causal research will power more granular and dynamic personalization strategies, moving beyond simple segmentation to deliver truly individualized experiences based on each customer‘s unique causal drivers of behavior.

Augmented causal analytics: The convergence of causal inference, machine learning, and natural language processing will give rise to AI-powered marketing analytics platforms that automatically surface causal insights and recommendations in intuitive, actionable formats.

To thrive in this new era of causal-driven marketing, organizations must cultivate a culture of continuous experimentation, data-driven decision-making, and agile execution. This requires investments in the right skills, tools, and processes, as well as a willingness to challenge assumptions and embrace change.

In the fast-moving, hyper-competitive world of marketing, causal research has emerged as a vital tool for driving growth, innovation, and customer value. By rigorously investigating the cause-and-effect relationships that underlie marketing performance, causal research empowers marketers to make smarter, more confident decisions and optimize their strategies for maximum impact.

As we‘ve seen in this comprehensive guide, causal research is a powerful yet complex undertaking, requiring a systematic process, robust methods, and careful interpretation. But for marketers willing to invest the time and effort, the rewards are substantial: deeper customer insights, more effective campaigns, and sustainable competitive advantage.

To get started with causal research in your own marketing organization, follow these key steps:

• Identify your most pressing marketing challenges and opportunities
• Develop a causal research plan, including clear hypotheses, variables, and experiments
• Invest in the necessary tools, talent, and partnerships to execute your research effectively
• Analyze and interpret your findings, translating insights into actionable strategies and tactics
• Continuously test, learn, and refine your approach based on evolving customer needs and market conditions

By embracing causal research as a core marketing capability, you‘ll be well-positioned to navigate the challenges and seize the opportunities of 2024 and beyond. So start experimenting, keep learning, and let causal insights be your guide on the path to marketing success.

Causal Research: Definition, Design, Tips, Examples

Appinio Research · 21.02.2024 · 34min 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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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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That actually explain what's on your next test, causal research, from class:, principles of marketing.

Causal research is a type of marketing research that aims to identify cause-and-effect relationships between variables. It seeks to determine the impact that changes in one factor have on another, providing insights into how and why certain marketing phenomena occur.

congrats on reading the definition of Causal Research . now let's actually learn it.

5 Must Know Facts For Your Next Test

• Causal research is often used to test hypotheses and determine the impact of marketing strategies, such as the effect of a new product feature on sales.
• This type of research involves the identification of independent and dependent variables, and the use of statistical techniques to establish causality.
• Causal research is more complex and time-consuming than descriptive or exploratory research, as it requires rigorous experimental design and data analysis.
• Findings from causal research can be used to make informed decisions about marketing actions and resource allocation.
• Causal research is essential for understanding the underlying mechanisms and drivers of consumer behavior, which can inform the development of effective marketing campaigns.

Review Questions

• Causal research is distinct from descriptive and exploratory research in its focus on establishing cause-and-effect relationships between variables. While descriptive research aims to describe market characteristics and exploratory research seeks to gain a preliminary understanding of a problem, causal research involves the manipulation of independent variables to observe their impact on dependent variables. This allows marketers to identify the underlying drivers of consumer behavior and the effectiveness of marketing strategies, providing valuable insights to inform decision-making and resource allocation within a successful marketing research plan.
• Experimental design is a crucial component of causal research, as it involves the systematic manipulation of independent variables to observe their effect on dependent variables. This approach allows marketers to establish clear causal relationships, moving beyond simply describing or exploring a phenomenon. Within the steps of a successful marketing research plan, experimental design is essential for testing hypotheses, identifying the impact of marketing strategies, and generating actionable insights that can inform decision-making. By carefully controlling the research environment and isolating the variables of interest, causal research using experimental design provides the most robust and reliable evidence to guide marketing decisions.
• Incorporating causal research into a successful marketing research plan can provide significant benefits, but also presents certain challenges. The key benefit of causal research is its ability to establish clear cause-and-effect relationships, allowing marketers to understand the underlying drivers of consumer behavior and the impact of their strategies. This can lead to more informed decision-making, more effective resource allocation, and ultimately, improved marketing outcomes. However, causal research is generally more complex and time-consuming than descriptive or exploratory research, requiring rigorous experimental design and statistical analysis. Additionally, the establishment of causality can be influenced by various confounding variables, necessitating careful control and consideration. Despite these challenges, the insights gained from causal research can greatly enhance the overall effectiveness of a marketing research plan by providing a deeper understanding of the mechanisms that shape consumer attitudes and behaviors, leading to more strategic and impactful marketing initiatives.

Related terms

Descriptive Research : Descriptive research focuses on describing the characteristics of a market or a population, without establishing causal relationships.

Exploratory Research : Exploratory research is used to gain a preliminary understanding of a problem or opportunity, often when the specific issues are not well defined.

Experimental Design : Experimental design involves the manipulation of one or more independent variables to observe their effect on a dependent variable, establishing causal relationships.

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• Advertising Strategy & Consumer Insights
• Fundamentals of Marketing
• Intrapreneurship
• Market Research: Tools and Techniques for Data Collection and Analysis
• Marketing Research
• Marketing Strategy

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What is causal research design?

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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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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.

Understanding Causal Research: Definition, Examples, and Applications

Causal research is a type of investigation that seeks to establish a cause-and-effect relationship between variables. It aims to determine whether changes in one variable (the independent variable) lead to changes in another variable (the dependent variable). This method of research is crucial in understanding the reasons behind certain phenomena and predicting outcomes based on identified causal relationships.

Table of Contents

1. key concepts and characteristics.

• Cause and Effect: Causal research focuses on identifying and understanding the causal relationships between variables. It investigates how changes in one variable influence changes in another.
• Experimental Design: Often involves experimental methods where researchers manipulate one variable (independent variable) and observe its effect on another variable (dependent variable).
• Controlled Environment: Requires controlling for other potential influencing factors to isolate the effects of the independent variable.
• Quantitative Analysis: Typically involves quantitative data analysis to measure and quantify the relationship between variables statistically.

2. Examples of Causal Research

Practical applications:.

• Marketing: A company conducts causal research to determine whether changes in pricing (independent variable) affect sales volume (dependent variable). By running experiments or using historical data, they can establish the impact of pricing changes on consumer behavior.
• Healthcare: Researchers study the effects of a new drug (independent variable) on patient recovery time (dependent variable). Through controlled trials, they assess whether the drug causes a significant improvement compared to a placebo or existing treatments.
• Education: Educational researchers investigate the impact of teaching methods (independent variable) on student performance (dependent variable) to identify the most effective teaching strategies.

3. Methods Used in Causal Research

Experimental and non-experimental approaches:.

• Experimental Design: Involves manipulating the independent variable and observing changes in the dependent variable under controlled conditions.
• Quasi-experimental Design: Uses natural variations or existing conditions to study causal relationships, often without random assignment.
• Longitudinal Studies: Track changes in variables over time to establish causal relationships based on observed patterns and correlations.

4. Steps Involved in Conducting Causal Research

Methodological approach:.

• Formulate Hypotheses: Develop clear hypotheses about the relationship between the independent and dependent variables.
• Design Experiments: Plan experimental or observational methods to manipulate and measure variables.
• Collect Data: Gather quantitative data through surveys, experiments, or observations.
• Analyze Data: Use statistical techniques to analyze the data and determine the strength and significance of the causal relationship.
• Draw Conclusions: Interpret findings to draw conclusions about whether a causal relationship exists and the nature of that relationship.

5. Significance and Applications

Importance in research and decision-making:.

• Predictive Power: Helps predict outcomes based on identified causal relationships, informing strategic decisions in various fields.
• Policy Implications: Influences policy decisions by providing evidence of what interventions or changes are likely to produce desired outcomes.
• Business Strategy: Guides businesses in optimizing processes, products, and marketing strategies based on scientifically validated cause-and-effect relationships.

6. Conclusion

Causal research plays a vital role in advancing knowledge and understanding cause-and-effect relationships in diverse disciplines. By rigorously testing hypotheses and establishing causal links between variables, researchers and practitioners can make informed decisions, drive improvements, and innovate in their respective fields. Understanding the principles and methods of causal research is essential for anyone involved in scientific inquiry, policy development, or strategic planning where understanding causality is critical for success.

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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.

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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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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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Table of Contents

• Research Methods in Business and Management

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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.

• Causal Marketing
• Product Research
• Media Research
• Communication Research
• Values And Lifestyles (VALS) Research
• Distribution Research

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.

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Casual Inference

What is causal inference.

Causal inference is the process of identifying and quantifying the causal effect of one variable on another. It involves using statistical methods, study designs, and theoretical frameworks to establish causality while accounting for confounding factors, potential biases, and the limitations of observational data.

The Basic Idea

Many of the questions we ask have to do with cause and effect. What causes hiccups? Does raising the minimum wage cause unemployment? Will exposure to germs build immunity? Does social media contribute to anxiety? Can eating raw garlic help acne? We always want to understand why things happen or what causes certain phenomena. And our fascination with these questions is not unjustified! Identifying the source of an effect is critical to our understanding of the world. More than simply satisfying our curiosity, it informs decision-making in our individual lives and on a much larger, societal scale.

But how do we decipher these cause-and-effect relationships? This is where causal inference comes in. Causal inference is a methodological approach that spans various disciplines, including statistics, computer science, psychology, and social sciences. It involves using methods, processes, and theoretical frameworks for identifying causality or determining whether a cause-and-effect relationship occurs between two variables. 1 Importantly, causal inference aims to eliminate all other possible causes for the observed effect, which is understandably challenging when exploring complex systems like healthcare, economics, and environmental science.

Causal inference is easiest to attain in clinical or laboratory settings. The gold-standard research method for identifying a causal relationship is the randomized controlled trial (RCT) where a treatment (a single variable of interest) is randomly assigned to participants. At the same time, all other factors are held constant. If the study shows a statistically significant effect from manipulating the variable, there is evidence of a causal relationship.

Unfortunately, it can be difficult—and even unethical—to conduct large-scale experiments that accurately reflect real-world phenomena. Complex systems like social programs or public health initiatives are not great candidates for RCTs. The good news is that researchers have developed tools and methods to infer causation in observational settings where RCTs are not possible. 2

Example: Air Pollution and Respiratory Disease

Say researchers want to determine if air pollution causes an increase in respiratory diseases in a particular city. Simply running a traditional statistical analysis might identify a relationship between these variables, but this is not enough to infer causality . Perhaps you’ve heard the cautionary phrase, “correlation does not imply causation,” which sums up how a relationship between two variables does not necessarily mean that one variable causes the other.

In our example, researchers would use causal inference methods to estimate the effect of air pollution on the rate of respiratory disease while controlling for potential confounding variables that may influence the relationship. These methods might include:

• Directed Acyclic Graphs (DAGs): Visual tools that map the relationship between variables. 3 For example, researchers might create a DAG to show connections between air pollution, respiratory disease, and other related variables like weather patterns, seasons, smoking rates, and cardiovascular health. This would help the researchers determine which variables need to be controlled for when estimating a causal effect.
• Propensity Score Analysis: A statistical matching technique used to estimate the effect of an intervention (in this case, air pollution) by attempting to isolate it from other variables. In our example, this might mean identifying people with similar likelihoods of being exposed to air pollution—based on characteristics like income and smoking habits—and then comparing their health outcomes.
• Time-Series Analysis: A method used to analyze data collected over time and identify patterns. For instance, historical data could help researchers identify if higher levels of air pollution were followed by higher rates of respiratory disease in the past.
• Difference in Differences: A statistical technique that compares outcomes between a treatment group and a control group over time. In our example, researchers might compare changes in respiratory disease rates in a city where air pollution levels spiked to a similar city where they stayed constant. If disease rates increased in the former city but stayed constant in the latter, it could suggest a causal relationship.

These methods—and many others—allow researchers to draw conclusions about causality in complex settings, providing us with a better understanding of real-world causal relationships.

Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why. Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it. Judea Pearl, The Book of Why: The New Science of Cause and Effect

Correlation: A statistical relationship between two variables in which a change in one variable is associated with a change in the other. Importantly, correlation does not imply that one variable caused the other to change.

Causation: Indicates that one variable directly affects another. Unlike correlation, causation suggests a cause-and-effect relationship between two variables.

Randomized Controlled Trial (RCT): A study design that involves randomly assigning participants to an experimental group that receives treatment or a control group that does not receive treatment. As a result, the only expected difference between the two groups is the variable being studied. This allows researchers to attribute differences in the outcome to the variable itself.

Quasi-Experiment: A study design used to estimate the causal effect of a variable on a target population without randomly assigning participants to treatment or control groups. Quasi-experiments offer an alternative to RCTs for estimating cause-and-effect relationships through observational data. 4

The Counterfactual Model: Also called the potential outcome model, the counterfactual model allows researchers to predict how an outcome might change under different hypothetical circumstances and compare these differences to infer causality. 5

Confounding Variables: Also called confounders, confounding variables are unmeasured variables that influence the relationship between the variables being studied. In a study examining the effects of smoking on heart disease, confounders might include age and socioeconomic status.

Sensitivity Analysis: A process of assessing the sensitivity of study results to potential unobserved confounding variables. 6 Sensitivity analysis techniques are used to determine how strong the effects of an unobserved confounder would have to be to change the outcome. This is key when using non-experimental methods where it’s difficult to control for confounders.

Causality has been discussed among scientific researchers for centuries. For instance, Koch’s postulates, a set of criteria used to assess whether a microorganism causes a disease, have been used since the 19th century to determine if certain microbes cause certain diseases. 7 These early types of methods for establishing causality differ from modern, formal methods of causal inference that rely on more reliable statistical approaches.

Sewall Wright was one of the first researchers to articulate causal assumptions mathematically. 8 In 1921, Wright introduced a method called path diagrams to extract causation from correlational data and represent causal relationships on directed graphs. His work laid the foundation for future causal modeling techniques.

Around the 1970s, statistics professor Donald Rubin introduced a framework called the Rubin causal model (or the Neyman-Rubin causal model) built on earlier work by Jerzy Neyman. This model was designed to determine causality in observational and experimental studies. More specifically, it provides a method for researchers to compare potential outcomes of hypothetical scenarios, or counterfactuals, even though only one outcome can be observed in reality.

Later, in the late 20th century, computer scientist Judea Pearl introduced directed acyclic graphs (DAGs). As touched on earlier, these diagrams visually represent causal relationships and indicate the direction of causality between variables. 8 DAGs allow researchers to identify confounding variables, which is essential for distinguishing causation from correlation and predicting the effects of interventions through counterfactual reasoning. 

Consequences

Public Health

Introducing causal inference to scientific research has significant public health consequences. By uncovering causal connections between risk factors and diseases, researchers can explore potential interventions and reduce rates of illness. 9 For example, identifying smoking as a cause of lung cancer led to public health interventions like cigarette taxes and smoking cessation programs which helped reduce lung cancer rates.

Causal inference is also valuable for evaluating how effective certain interventions might be. In drug discovery and development, causal inference techniques help researchers predict the outcomes of medical interventions and determine which interventions are most effective before implementing them at scale. 10

Business Research

Today, many businesses conduct their own research, often in the form of randomized A/B tests . While A/B tests are perfect for determining which variant of a product or marketing strategy performs best, they aren’t great at capturing long-term effects or providing insight into complex interactions—like how combined changes to pricing and product features might impact customer engagement. This is where quasi-experiments and causal inference methods can benefit business.

Companies like Netflix, Amazon, and Uber have embraced causal inference methods to determine how certain business decisions or marketing moves might impact consumer behavior. For instance, Uber mentions using causal inference to determine how delivery delays might affect a customer’s future engagement with the platform. 11 Causal inference can be a valuable tool to help businesses answer these kinds of questions by examining real-world user data rather than running experiments—which, in this case, would involve purposely delaying deliveries to study the impact on customer experience.

The Human Element

People naturally think in terms of cause and effect. We’re predisposed to seek evidence for why things occur as this helps us navigate the world and make predictions about the future—hence our fascination with “why” questions. Due to our tendency to infer cause, causal inference plays an incredibly important role in helping us distinguish between correlation and causation in research. The goal? Extract real truth from scientific studies.

Unfortunately, people still tend to make causal claims based on correlational evidence. We see this frequently in the media reports of scientific findings where headlines often mention causal relationships when there is only correlational evidence. In fact, a large body of research finds that causal theory errors are common when interpreting scientific findings. 12

This is a significant problem. People often make decisions about what to do or what to believe based on research findings reported by the media. Can our reasoning be improved so we can avoid jumping to causal conclusions when we only have evidence of a correlational relationship?

One recent study explored this phenomenon among college students to identify potential educational interventions to improve our reasoning. 12 The researchers noted that behavioral science students often interpret correlational findings with fairly low rates of error, specifically after taking psychology classes that expose them to correlational and experimental studies. The study found that a short and simple educational intervention about causal theory error significantly improved the students’ abilities to accurately distinguish between correlation and causation. 

Being able to spot and understand true causal relationships is key to making decisions based on research. This is crucial for individual consumers, but also for business leaders and policymakers involved in designing and implementing interventions for business and societal issues.

Controversies

The Accuracy of Causal Inference

While causal inference is a valuable tool for understanding relationships between variables, it does come with a few key issues. For instance, debates often involve disagreements over the efficacy of causal inference methods. Critics point out weaknesses in determining causality in complex environments, such as when studying real-world observational data or when relying on the counterfactual model to make predictions about the future.

Controlling for confounding variables is particularly difficult when conducting quasi-experiments. In these experiments, causal inference often involves making assumptions when interpreting data, and this introduces the risk of bias. 13 Some critics even worry that researchers could purposely manipulate statistical analyses to show causation where none exists. As with much of science, the field of causal inference is still developing new methodological tools to address the challenges and increase the validity of causal inference research.

Using AI to Predict Behavior

Artificial intelligence (AI) is often used to predict behavior. Unfortunately, like us humans, AI tools can fall into the same trap of equating correlation with causation. Here’s an example: machine learning programs are often used in risk prediction software to estimate people’s future medical needs. One widely used algorithm attempts to flag patients who would benefit from extra medical care now based on how likely they are to require medical care in the future. 15 The problem? An analysis of this particular software showed that black patients with more chronic illnesses than white patients were not flagged as needing extra care. 

This occurred because the algorithm used insurance claims to predict people’s future health needs, but did not account for the fact that healthcare spending is typically lower on black Americans than white Americans. This could be due to confounding variables like lack of insurance or systemic barriers to healthcare access. The algorithm assumed that lower healthcare spending meant people had fewer health conditions, but this causal relationship was not accurate for all populations.

The ultimate issue with relying on AI for causal inference is that predictive AI recognizes patterns, but this can lead to incorrect conclusions about causation. This issue has led to the development of causal AI programs that rely on causal models to identify cause-and-effect relationships rather than just correlational evidence. These programs are currently being explored to simulate scenarios and compare the potential effect of different interventions on an outcome.

Do Short-Term Rentals Affect Rent Prices?

This Airbnb case study is an excellent illustration of the benefits of causal inference for distinguishing causation from correlation. In October 2023, the Conference Board of Canada released a report analyzing the impact of short-term rentals on rental prices across the country.15 They tested for a causal link between Airbnb activity and rent increases between 2016 and 2022 across 330 neighborhoods in 19 Canadian cities.

As you might expect, they observed a correlational relationship—Airbnb activity and rental prices rose together during this period. But when testing for a causal link, researchers found no meaningful impact of Airbnbs on rent increases. Of the 30% increase in rents over this time, at most less than 1% could be attributed to Airbnb activity.

This research follows moves from several Canadian cities and provinces to implement policies to regulate short-term rentals. While these policies have significantly reduced Airbnb activity (by nearly 50%), there is no evidence that this has resulted in lower rents. 

These results may be surprising. For years, people have been concerned that Airbnb activity is reducing the housing supply and causing rents to surge. This report highlights the importance of using causal inference to determine causation instead of making assumptions about causation based on correlational evide

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• Pearl, J. (2010). An Introduction to Causal Inference. The International Journal of Biostatistics , 6 (2). https://doi.org/10.2202/1557-4679.1203
• Plümper, T., Troeger, V. E., & Neumayer, E. (2019). Case selection and causal inferences in qualitative comparative research. PLoS ONE , 14 (7). https://doi.org/10.1371/journal.pone.0219727
• Tilden, E. L., & Snowden, J. M. (2018). The causal inference framework: A primer on concepts and methods for improving the study of well-woman childbearing processes. Journal of Midwifery & Women's Health , 63 (6), 700. https://doi.org/10.1111/jmwh.12710  
• Moss, H. A., Melamed, A., & Wright, J. D. (2019). Measuring cause-and-effect relationships without randomized clinical trials: Quasi-experimental methods for gynecologic oncology research. Gynecologic Oncology , 152 (3), 533-539. https://doi.org/10.1016/j.ygyno.2018.11.006
• Höfler, M. (2005). Causal inference based on counterfactuals. BMC Medical Research Methodology , 5 , 28. https://doi.org/10.1186/1471-2288-5-28
• Liu, W., Kuramoto, S. J., & Stuart, E. A. (2013). An Introduction to Sensitivity Analysis for Unobserved Confounding in Non-Experimental Prevention Research. Prevention Science : The Official Journal of the Society for Prevention Research , 14 (6), 570. https://doi.org/10.1007/s11121-012-0339-5  
• Segre, J. A. (2013). What does it take to satisfy Koch’s postulates two centuries later? Microbial genomics and Propionibacteria acnes. The Journal of Investigative Dermatology , 133 (9), 2141. https://doi.org/10.1038/jid.2013.260
• Pearl, J. (2022). Causal Inference: History, Perspectives, Adventures, and Unification (An Interview with Judea Pearl). Observational Studies 8 (2), 23-36. https://dx.doi.org/10.1353/obs.2022.0007
• Glass, T. A., Goodman, S. N., Hernán, M. A., & Samet, J. M. (2013). Causal Inference in Public Health. Annual Review of Public Health , 34 , 61. https://doi.org/10.1146/annurev-publhealth-031811-124606
• Michoel, T., & Zhang, J. D. (2023). Causal inference in drug discovery and development. Drug Discovery Today , 28 (10), 103737. https://doi.org/10.1016/j.drudis.2023.103737
• Harinen, T., & Li, B. (2019, June 19). Using Causal Inference to Improve the Uber User Experience . Uber. Retrieved August 20, 2024, from https://www.uber.com/en-CA/blog/causal-inference-at-uber/
• Seifert, C. M., Harrington, M., Michal, A. L., & Shah, P. (2022). Causal theory error in college students’ understanding of science studies. Cognitive Research: Principles and Implications , 7 . https://doi.org/10.1186/s41235-021-00347-5
• Hammerton, G., & Munafò, M. R. (2021). Causal inference with observational data: The need for triangulation of evidence. Psychological Medicine , 51 (4), 563-578. https://doi.org/10.1017/S0033291720005127
• Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review, 18 (3), 50-55. https://doi.org/10.48558/KT81-SN73  
• Conference Board of Canada (2023). Airbnb activity and rental markets in Canada: Analyzing the impact of short-term rentals . The Conference Board of Canada.

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Annika Steele

Annika completed her Masters at the London School of Economics in an interdisciplinary program combining behavioral science, behavioral economics, social psychology, and sustainability. Professionally, she’s applied data-driven insights in project management, consulting, data analytics, and policy proposal. Passionate about the power of psychology to influence an array of social systems, her research has looked at reproductive health, animal welfare, and perfectionism in female distance runners.

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