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Department of Statistics, UCLA

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Probabilities of Causation: Three Counterfactual Interpretations and their Identification


According to common judicial standard, judgment in favor of plainti should be made if and only if it is "more probable than not" that the defendant's action was the cause for the plainti '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 su cient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or su cient) 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, we show that necessity and su ciency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for speci c scenarios.

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