RAND Statistics Seminar Series

Reasoning with Cause and Effect

Presented by Judea Pearl
University of California, Los Angeles

December 9, 2004, 10:30 a.m.
Forum m-1226 - Santa Monica

Abstract

The talk will review concepts, principles, and mathematical tools that were found useful in applications involving causal inference. The principles are based on structural-model semantics, in which functional (or counterfactual) relationships, representing autonomous physical processes are the fundamental building blocks. This semantical framework, enriched with a few ideas from logic and graph theory, enables one to interpret and assess a wide variety of causal and counterfactual relationships from various combinations of data and theoretical modeling assumptions.

These include:

1. Predicting the effects of actions and policies
2. Identifying causes of observed events
3. Assessing direct and indirect effects
4. Assessing the extent to which causal statements are corroborated by data
5. Assessing explanations of events in a specific scenario

For background information, see Causality (Cambridge University Press, 2000), http://www.cs.ucla.edu/~judea/, or the following papers:

• http://bayes.cs.ucla.edu/IJCAI99/
• http://bayes.cs.ucla.edu/R218-B.pdf
• ftp://ftp.cs.ucla.edu/pub/stat_ser/R271.pdf
• ftp://ftp.cs.ucla.edu/pub/stat_ser/R273.pdf