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) Assesseing the extent to which causal statements
are corroborated by data
For background information, see Causality (Cambridge University Press, 2000), or www.cs.ucla.edu/~judea/, or the following
papers:
gentle-introduction
(link to) http://bayes.cs.ucla.edu/IJCAI99/
paper1
(link to) http://bayes.cs.ucla.edu/R218-B.pdf
paper2
(link to) ftp://ftp.cs.ucla.edu/pub/stat_ser/R271.pdf
paper3
(link to) ftp://ftp.cs.ucla.edu/pub/stat_ser/R273.pdf
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