2005/11/09 by Joseph Y. Halpern, Judea Pearl · 17 citations
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Advanced Causal Inference Techniques
paper · doi:10.1093/bjps/axi148
We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature. 1. Introduction2. Causal models and the definition of actual causality: a review2.1Causal models2.2Syntax and semantics2.3The definition of cause3. Explanation: the basic definition4. Partial explanations and explanatory power5. The general definition6. DiscussionAppendix: the formal definition of causality