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
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.
- Predicting the effects of actions and policies
- Identifying causes of observed events
- Assessing direct and indirect effects
- Assessing the extent to which causal statements are corroborated by data
- 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: