Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification

According to common judicial standard, judgment in favor of plaintii should be made if and only if it is \more probable than not" that the defendant's action was the cause for the plaintii's damage (or death). This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or suucient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or suucient) causation can be learned from statistical data, and shows how data from both experimental and nonex-perimental studies can be combined to yield information that neither study alone can provide. Finally, w e s h o w that necessity and suuciency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for speciic scenarios.