3. The Foundations of Causal Inference

2010/08/01 by Judea Pearl
Mathematics · Psychology · Social Sciences · #Advanced Causal Inference Techniques #Artificial intelligence #Causal inference #Causal model #Computer science #Counterfactual conditional #Counterfactual thinking #Econometrics #Economics #Generalization #Inference #Management science #Matching (statistics) #Mathematical economics #Mathematics #Mediation #Nonparametric statistics #Observational study #Outcome (game theory) #Propensity score matching #Psychology #Qualitative Comparative Analysis Research #Social psychology #Social science #Sociology #Statistical Methods and Inference #Statistics

paper · doi:10.1111/j.1467-9531.2010.01228.x

crossref issued 2010/08/01 · crossref published 2010/08/01 · crossref published-print 2010/08/01 · openalex publication_date 2010/08/01 · crossref published-online 2010/10/14 · crossref created 2010/10/14 · openalex created_date 2025/10/10 · crossref deposited 2026/05/01 · crossref indexed 2026/07/31 · openalex updated_date 2026/07/31

Abstract

This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating, and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM)—a natural generalization of those usedby econometricians and social scientists in the 1950s and 1960s, which provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potential-outcome frameworks, and develops symbiotic tools that use the strong features of both.

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