I will review concepts, principles, and mathematical tools that were found useful in applications involving causal reasoning. 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

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

gentle-introduction http://bayes.cs.ucla.edu/IJCAI99/
paper1 http://bayes.cs.ucla.edu/R218-B.pdf
paper2 ftp://ftp.cs.ucla.edu/pub/stat_ser/R271.pdf
paper3 ftp://ftp.cs.ucla.edu/pub/stat_ser/R273.pdf

Judea Pearl is a professor of computer science and statistics at the University of California, Los Angeles. He joined the faculty of UCLA in 1970, where he currently directs the Cognitive Systems Laboratory and conducts research in artificial intelligence and philosophy of science.  He has authored three books, Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000).   A member of the National Academy of Engineering, a Fellow of the IEEE and a Founding Fellow the American Association for Artificial Intelligence (AAAI), Judea Pearl is the recipient of the IJCAI Research Excellence Award for 1999, the AAAI Classic Paper Award for 2000 and the Lakatos Award for 2001.