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This paper discusses causal independence models and a generalization of thesemodels called causal interaction models. Causal interaction models are modelsthat have independent mechanisms where a mechanism can have several causes. Inaddition to introducing several particular types of causal interaction models,we show how we can apply the Bayesian approach to learning causal interactionmodels obtaining approximate posterior distributions for the models and obtainMAP and ML estimates for the parameters. We illustrate the approach with asimulation study of learning model posteriors.

Structure and Parameter Learning for Causal Independence and Causal Interaction Models