Causal Research (Explanatory research)
Causal research, also known as explanatory research, is a type of research that aims to identify and explain cause-and-effect relationships between variables. Its primary objective is to determine whether changes in one variable produce changes in another and to understand the mechanisms through which those changes occur.
On this page:
- What is causal research?
- Components of causal research
- Causal research methods
- Examples of causal research methods
- Causal research in the age of AI and digital business
- Advantages and disadvantages
- When to use causal research
- Exam tip
| Feature | Causal Research | Exploratory Research | Descriptive Research |
|---|---|---|---|
| Main purpose | Explain cause-and-effect relationships | Explore unclear problems | Describe characteristics or situations |
| Main research statement | Hypothesis | Research question | Research question |
| Level of structure | Highly structured | Flexible and unstructured | Structured |
| Typical stage | Later stages of research | Early stages of research | Later stages of research |
| Typical methods | Experiments, quasi-experiments | Interviews, observations | Surveys, case studies |
Research designs at a glance
Causal research explains why something happens, whereas descriptive research explains what is happening.
What is Causal Research?
Businesses rarely make decisions based solely on descriptions of events. Managers usually want to know why something happened and what caused a particular outcome.
For example, if customer satisfaction increases after a company launches a new loyalty programme, managers need to know whether the programme actually caused the improvement or whether other factors were responsible. Similarly, if employee productivity rises following the introduction of remote working, researchers may want to determine whether remote working itself caused the improvement.
Causal research attempts to answer these questions by examining relationships between variables and identifying whether one factor influences another.
In simple terms, causal research seeks to establish whether a change in one variable produces a change in another variable.
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Components of Causal Research
Establishing a causal relationship requires more than simply observing that two variables are related. Researchers generally look for three key forms of causal evidence.
The first requirement is temporal sequence. The cause must occur before the effect. For example, if a company introduces a rebranding campaign in June and sales begin increasing in August, the temporal sequence requirement may be satisfied. However, if sales began increasing before the rebranding initiative, it would be difficult to argue that rebranding caused the improvement.
The second requirement is concomitant variation. Changes in one variable should be associated with systematic changes in another variable. If employee training increases and customer satisfaction improves consistently, this may suggest a potential causal relationship between the two variables.
The third requirement is non-spurious association. Researchers must demonstrate that the observed relationship is not being caused by another factor. For example, an apparent relationship between employee training and customer satisfaction may actually be influenced by improved management practices, new technology, or changes in product quality. Researchers therefore need to eliminate alternative explanations before drawing causal conclusions.
Only when all three conditions are satisfied can researchers make a strong case for causality.
Causal Research Methods
Several methods can be used to investigate causal relationships.
Experiments are generally regarded as the gold standard of causal research. Researchers manipulate an independent variable and observe its effect on a dependent variable while controlling for other influences. Because of this high level of control, experiments provide some of the strongest evidence for causality.
Quasi-experiments are used when random assignment is impractical or unethical. Instead of manipulating variables directly, researchers examine naturally occurring differences between groups and compare outcomes. Although less rigorous than experiments, quasi-experiments often provide valuable causal insights in real-world business settings.
Propensity Score Matching (PSM) is a statistical technique that attempts to reduce selection bias by matching participants or organisations with similar characteristics. This allows researchers to compare more equivalent groups and estimate causal effects more accurately.
Instrumental Variable Analysis is used when researchers suspect that hidden factors may influence the relationship between variables. An instrumental variable helps isolate the causal effect by providing an alternative source of variation in the independent variable.
Regression Discontinuity Design exploits naturally occurring cut-off points or thresholds. For example, if only employees with performance scores above a certain threshold receive additional training, researchers can compare individuals just above and below the threshold to estimate the effect of the intervention.
The choice of method depends on the research question, available data, practical constraints, and the level of causal evidence required.
Examples of Causal Research (Explanatory Research)
Causal research is commonly used to investigate the effects of one variable on another.
Examples of causal research objectives include:
- To assess the impact of foreign direct investment on economic growth in Taiwan.
- To evaluate the effects of rebranding initiatives on customer loyalty.
- To examine the influence of work process re-engineering on employee motivation.
- To investigate whether digital marketing campaigns increase customer engagement.
- To determine whether flexible working arrangements improve organisational performance.
- To assess the impact of AI implementation on employee productivity.
In each case, the objective is not simply to describe a phenomenon but to explain whether one factor causes changes in another.
Causal Research in the Age of AI and Digital Business
Advances in artificial intelligence, big data analytics, machine learning, and digital technologies have significantly increased the importance of causal research in modern business environments.
Organisations now collect vast amounts of data regarding customer behaviour, employee performance, operational efficiency, marketing effectiveness, and technology adoption. While AI systems are highly effective at identifying patterns and correlations within these datasets, they are often less effective at distinguishing genuine causal relationships from coincidental associations.
For example, an AI system may identify a strong relationship between customer engagement and social media activity. However, this does not necessarily mean that social media activity caused the increase in engagement. Other factors, such as pricing strategies, product improvements, seasonal trends, or broader economic conditions, may be influencing both variables.
Similarly, organisations implementing AI-powered recommendation systems may observe increased sales following deployment. Causal research helps determine whether the recommendation system genuinely produced the improvement or whether alternative explanations account for the observed outcome.
The same challenge applies to remote working technologies, digital transformation initiatives, employee monitoring systems, and AI-assisted decision-making tools. Managers increasingly need evidence that interventions actually cause desired outcomes before investing significant resources.
As organisations become more data-driven, the ability to distinguish causation from correlation is becoming one of the most valuable skills in business research and analytics. While AI can identify relationships efficiently, causal research provides the framework needed to evaluate whether those relationships represent genuine cause-and-effect mechanisms.
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Advantages and Disadvantages
A key strength of causal research is its ability to provide evidence-based explanations for organisational decisions. Rather than simply identifying patterns, researchers can investigate why those patterns occur and which factors are driving observed outcomes.
Another notable advantage is the relatively high level of internal validity associated with many causal research designs. Well-designed experiments and quasi-experiments can provide strong evidence regarding the direction and nature of causal relationships.
Researchers also value causal research because it often supports prediction and decision-making. Understanding causal relationships enables organisations to forecast the likely consequences of future actions and interventions.
Despite these benefits, causal research presents important challenges. A common misconception is that correlation automatically implies causation. Two variables may appear strongly related even when no genuine causal relationship exists. Coincidences and hidden variables can easily produce misleading conclusions.
Business environments introduce additional complexity because organisational outcomes are often influenced by multiple interacting factors. Employee motivation, for example, may be affected simultaneously by leadership style, compensation, organisational culture, economic conditions, career opportunities, and personal circumstances.
Researchers should also recognise that proving causality with complete certainty is often difficult. In many situations, variables influence one another in complex and dynamic ways, making it challenging to determine which variable is the cause and which is the effect.
Consequently, causal research usually provides evidence that supports causal explanations rather than absolute proof of causality.
When to Use Causal Research
You should use causal research if:
- your objective is to test cause-and-effect relationships
- you need evidence about the impact of one variable on another
- your study involves hypothesis testing
- variables can be measured systematically
- management decision-making requires causal evidence
- understanding why something happens is more important than simply describing what is happening
- you want to evaluate the effectiveness of a policy, intervention, programme, or innovation
Causal research is particularly valuable when organisations need reliable evidence regarding the consequences of business decisions, strategic initiatives, or technological changes.
Exam Tip
When discussing causal research in your dissertation:
- distinguish clearly between correlation and causation
- explain how causal evidence will be established
- justify the choice of causal research design
- discuss alternative explanations and confounding variables
- acknowledge limitations in proving causality within real-world settings
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[1] Source: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning