What is Causal inference?
Causal inference is the set of methods used to determine whether a relationship between variables is causal (A causes B) rather than merely correlational (A and B occur together). It is the foundation of evidence-based practice in behavioral science.
How it works
The gold standard for causal inference is the randomized controlled trial, which isolates the causal effect by randomly assigning participants to treatment and control groups. When randomization is impossible, quasi-experimental methods (difference-in-differences, regression discontinuity, instrumental variables) and observational methods (propensity score matching, directed acyclic graphs) attempt to approximate causal conclusions. The fundamental challenge is confounding: other variables that influence both the treatment and the outcome.
Applied example
A company sees that employees who use the gym earn higher salaries and concludes that exercise causes higher earnings. Causal inference methods reveal that the relationship is confounded by job level: senior employees have both higher salaries and more flexible schedules to exercise. Exercise does not cause higher pay.
Why it matters
Causal inference is the discipline that prevents ‘correlation equals causation’ errors, which are the most common and consequential mistakes in behavioral science and policy.
