The Mathematics of Causal Inference

• Activity Number: 403
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Judea Pearl*+
• Companies: UCLA
• Keywords: causal inference ; confounding ; counterfactuals ; mediation ; generizability ; missing data

Abstract

Recent developments in graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. I will review concepts, principles, and mathematical tools that were found useful in this transformation, and will demonstrate their applications in several data-intensive sciences. These include questions of confounding control, policy analysis, misspecification tests, mediation, missing-data heterogeneity and the integration of data from diverse studies. The following topics will be emphasized: 1. What every student should know about causal inference, and why it is not taught in Statistics 101. 2. The Mediation Formula, and what it tells us about "How nature works" 3. What mathematics can tell us about "external validity" or "generalizing across populations" 4. What population data can tell us about unsuspected heterogeneity. Reference: J. Pearl, Causality (Cambridge University Press, 2000) Background material for circulation: http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r379.pdf Working papers: http://bayes.cs.ucla.edu/csl_papers.html