I will review concepts, principles, and mathematical tools that were found useful in applications involving causal relationships.  The principles are based on structural-model semantics, in which functional (or counterfactual) relations represent autonomous physical processes. This semantical framework, enriched with a few ideas from logic and graph theory, gives rise to a complete, coherent, and friendly calculus of causation that unifies the graphical and counterfactual approaches to causation and resolves long-standing problems in several of the sciences. These include questions of confounding, causal effect estimation, policy analysis, 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

Tutorials:

http://bayes.cs.ucla.edu/IJCAI99/

ftp://ftp.cs.ucla.edu/pub/stat_ser/R271.pdf

ftp://ftp.cs.ucla.edu/pub/stat_ser/R273.pdf

Monday, April 23 , 2007
11:00 am - 12:15 pm

Skirkanich Hall
Berger Auditorium