Causaliteitsinference

Causaliteitsinference, or causal inference in English, is the field concerned with determining whether and how one variable causally affects another. It aims to distinguish causation from mere association and to estimate the magnitude of causal effects from data, using both experimental and observational sources. The term is used in Dutch-language literature as well as in international discussions of causal reasoning.

Foundations in causal inference include the potential outcomes framework and structural causal models. The potential outcomes approach defines causal effects as differences between outcomes that would be observed under different treatment conditions. Structural causal models, often represented by directed acyclic graphs, encode causal assumptions in a graphical form, enabling reasoning about identifiability and adjustment. Do-calculus provides formal rules for identifying causal effects from observational data given a causal graph. Key concerns include identifiability, exchangeability, and the proper specification of assumptions.

Common methods span experimental and observational designs. Randomized controlled trials offer high internal validity by random assignment. When experiments are impractical, observational tools—such as propensity score methods, matching, instrumental variables, regression discontinuity, and difference-in-differences—are employed. Modern practice also uses g-methods (g-formula) and targeted learning to estimate effects under complex settings, often alongside sensitivity analyses to assess robustness to assumption violations.

Applications of causal inference appear in medicine, economics, epidemiology, and public policy. Challenges include unmeasured confounding, selection bias, measurement error, and issues of transportability across populations. The field emphasizes transparent assumptions, rigorous estimation, and ethical interpretation of causal conclusions.