, Volume 12, Issue 2, pp 281–345

Statistics and causal inference: A review


DOI: 10.1007/BF02595718

Cite this article as:
Pearl, J. Test (2003) 12: 281. doi:10.1007/BF02595718


This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.

Key Words

Structural equation modelsconfoundingnoncompliancegraphical methodscounterfactuals

AMS subject classification


Copyright information

© Sociedad Española de Estadistica e Investigación Operativa 2003

Authors and Affiliations

  1. 1.Cognitive Systems Laboratory, Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA