Probabilistic Evaluation of Counterfactual Queries

Causal Reasoning and Uncertainty Management

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 12

Causal Reasoning

Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one’s belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted as an external action that forces the proposition A to be true, which is consistent with Lewis’ Miraculous Analysis. This formalism offers a concrete embodiment of the "closest world" approach which (I) properly reflects common understanding of causal influences, (2) deals with the uncertainties inherent in the world, and (3) is amenable to machine representation.