Causes and Explanations: A Structural-Model Approach. Part II: Explanations

Abstract

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 paper. 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.

Part I of the paper (on causes) is also on the arxiv. The two papers originally were posted as one submission. The conference version of the paper appears in IJCAI '01. This paper will appear in the British Journal for Philosophy of Science

References in corpus (2)

• Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
• Defining Explanation in Probabilistic Systems

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• Influence-Driven Explanations for Bayesian Network Classifiers
• A formalism for causal explanations with an Answer Set Programming translation