Structure and parameter learning for causal independence and causal interaction models

Authors:
Christopher Meek;David Heckerman
Affiliations:
Microsoft Research, Redmond WA;Microsoft Research, Redmond WA
Venue:
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Year:
1997

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Abstract

We begin by discussing causal independence models and generalize these models to causal interaction models. Causal interaction models are models that have independent mechanisms where mechanisms can have several causes. In addition to introducing several particular types of causal interaction models, we show how we can apply the Bayesian approach to learning causal interaction models obtaining approximate posterior distributions for the models and obtain MAP and ML estimates for the parameters. We illustrate the approach with a simulation study of learning model posteriors.