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Activity Number: 265
Type: Invited
Date/Time: Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
Sponsor: Council of Chapters
Abstract - #308051
Title: The Mathematics of Causal Inference in Statistics
Author(s): Judea Pearl*+
Companies: University of California, Los Angeles
Address: Cognitive Systems Lab, Los Angeles, CA, 90095,
Keywords: Neyman-Rubin Model ; Nonparametric structural equations ; Calculus of causation ; Causal effect estimation ; Instrumental variables ; Integration of data
Abstract:

The Neyman-Rubin (NR) model, through which statisticians were first introduced to causal analysis, suffers from two drawbacks: (1) It lacks formal underpinning and (2) it uses an opaque, unnatural notation for expressing causal assumptions. I will describe the mathematical basis of the NR model using nonparametric structural equations, and will provide a transparent mathematical notation for expressing and discerning assumptions. The result is a complete and coherent calculus of causation that has resolved several classical problems of interest, including questions of confounding, covariate selection, causal effect identification, legal responsibility, effect decomposition, instrumental variables and the integration of data from diverse studies. Reference: J. Pearl, Causality (Cambridge University Press, 2000) http://bayes.cs.ucla.edu/jp_home.html.

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Revised September, 2007