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.
J. Pearl/Causal inference in statistics 99. tions of attribution, i.e., whether one event can be deemed “responsible” for another. 2. From association to causation 2.1. The basic distinction: Coping with change The aim of standard statistical analysis, typified by …
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