Definition
Causal inference is the process of determining whether an intervention caused observed outcomes by establishing a credible counterfactual and ruling out alternative explanations. It answers the question: "Did our programme make a difference, or would these outcomes have occurred anyway?"
Causal inference goes beyond correlation or association. It requires constructing or approximating what would have happened in the absence of the intervention (the counterfactual), then comparing actual outcomes against this alternative reality. The strength of causal claims depends on the credibility of the counterfactual and the extent to which alternative explanations have been systematically ruled out.
Why It Matters
Causal inference is essential when stakeholders need defensible evidence that a programme achieved its intended effects. Without it, you can only describe what happened, not what your programme achieved.
Donors, funders, and decision-makers increasingly require causal evidence before scaling programmes or continuing investments. Correlation alone is insufficient because observed changes may result from:
- Secular trends: broader economic, political, or social forces affecting outcomes independently of your programme
- Selection bias: systematic differences between programme participants and non-participants that affect outcomes
- External shocks: events like market changes, climate events, or policy shifts that affect all groups
- Maturation: natural changes that occur over time regardless of intervention
When the question is whether your programme "made a difference" or "caused improvement," causal inference provides the methodological foundation for answering that question credibly.
In Practice
Causal inference appears across multiple evaluation approaches, each with different strengths and feasibility constraints:
Randomised Controlled Trials (RCTs) create the strongest causal claims through random assignment, ensuring treatment and control groups are statistically equivalent at baseline. Any post-intervention differences can be attributed to the programme with high confidence.
Quasi-experimental designs approximate causal inference when randomisation is not feasible. Methods include:
- Propensity score matching, comparing participants with non-participants who have similar observable characteristics
- Regression discontinuity, exploiting arbitrary eligibility cutoffs to create comparable groups
- Difference-in-differences, comparing changes over time between treatment and control groups
Contribution analysis offers an alternative when counterfactual-based methods are impractical. It builds a causal story by gathering evidence that alternative explanations have been ruled out, rather than through direct comparison groups.
Process tracing examines the internal causal mechanisms, whether the expected pathway from activities to outcomes actually occurred as theorised.
The choice of method depends on feasibility, ethics, resources, and the strength of attribution required. Stronger causal claims require more resources but provide greater confidence in programme effectiveness.
Related Topics
- Attribution vs Contribution, Understanding the distinction between counterfactual-based and narrative-based causal claims
- Counterfactual, The comparison condition that makes causal inference possible
- Impact Evaluation, Evaluations specifically designed to establish causal attribution
- Quasi-Experimental Design, Methods for causal inference without randomisation
- Statistical Significance, Determining whether observed effects are likely real or due to chance
- Bias, Systematic errors that threaten causal inference validity
Further Reading
- Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences, Technical foundation for counterfactual reasoning and causal inference methods.
- Heller, T., et al. (2020). Causal Inference in Program Evaluation, RAND Corporation guide to causal methods for development programmes.
- BetterEvaluation: Causal Inference, Overview of methods for establishing causality in evaluation practice.
- What Works Clearinghouse Procedures and Standards Handbook, U.S. Department of Education standards for causal evidence in education interventions.