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Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification

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Abstract

According to common judicial standard, judgment in favor ofplaintiff should be made if and only if it is “more probable than not” thatthe defendant's action was the cause for the plaintiff's damage (or death). This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient)causation can be learned from statistical data, and shows how data fromboth experimental and nonexperimental studies can be combined to yieldinformation that neither study alone can provide. Finally, we show thatnecessity and sufficiency are two independent aspects of causation, andthat both should be invoked in the construction of causal explanations for specific scenarios.

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  1. Cognitive Systems Laboratory Computer Science Department, University of California, Los Angeles, Los Angeles, CA, 90024, USA

    Judea Pearl

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Pearl, J. Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification. Synthese 121, 93–149 (1999). https://doi.org/10.1023/A:1005233831499

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