Transcription of Causal inference in statistics: An overview
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Causal inference in statistics: An overview
Statistics SurveysVol. 3 (2009) 96 146 ISSN: 1935-7516 inference in statistics: An overview Judea PearlComputer Science DepartmentUniversity of California, Los Angeles, CA 90095 review presents empirical researcherswith recent advancesin Causal inference , and stresses the paradigmatic shifts that must be un-dertaken in moving from traditional statistical analysis to Causal analysis ofmultivariate data. Special emphasis is placed on the assumptions that un-derly all Causal inferences, the languages used in formulating those assump-tions, the conditional nature of all Causal and counterfactual claims, andthe methods that have been developed for the assessment of such advances are illustrated using a general theory of causation basedon the Structural Causal Model (SCM) described inPearl(2000a), whichsubsumes and unifies other approaches to causation, and provides a coher-ent mathematical foundation for the analysis of causes and particular, the paper surveys the development of mathematical tools forinferring (from a combination of data and assumptions) answers to threetypes of Causal queries: (1) queries about the effects of potential interven-tions, (also called Causal effects)
∗Portions of this paper are based on my book Causality (Pearl, 2000, 2nd edition 2009), and have benefited appreciably from conversations with readers, students, and colleagues. † This research was supported in parts by an ONR grant #N000-14-09-1-0665.
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