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

2005The British Journal for the Philosophy of Science引用 243
Bayesian Modeling and Causal InferenceAdvanced Causal Inference Techniques

摘要

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. Introduction 2. Causal models and the definition of actual causality: a review2.1Causal models 2.2Syntax and semantics 2.3The definition of cause 3. Explanation: the basic definition 4. Partial explanations and explanatory power 5. The general definition 6. Discussion Appendix: the formal definition of causality