FROM BAYESIAN NETWORKS
ACTIONS, CAUSES, AND COUNTERFACTUALS
Computer Science Department
Bayesian networks were introduced in the early 1980's
to facilitate the representation of probability functions.
Today, they are steadily giving way to a more flexible
representation, called "modifiable structural model", which
permits us to compute probabilities not merely for
(conditional) events, but also for the effects of
new actions (e.g., P(B=true)|do(A=true))), and for counterfactual
sentences (e.g., "B would have been different if A were true").
The talk will describe the mathematical foundations of modifiable
structural models, their axiomatic characterization, and their
relations to the causal theories of Simon (1953),
Good (1961), Suppes (1970) and Lewis (1973).
Applications in AI, statistics, epidemiology, law,
and economics will be demonstrated.
Reference material can be found in
Technical Reports R-218, R-248, R-236T, R-240, R-250.
Last modified: Thu May 7 22:53:31 PDT 1998