Artificial Intelligence - Special issue on relevance
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Causes and Explanations: A Structural-Model Approach: Part 1: Causes
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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Within the law, the traditional test for attributing causal responsibility is the "but-for" test, which asks whether, 'but for' the defendant's wrongful act, the injury complained of would have occurred. This definition conforms to common intuitions regarding causation, but gives non-intuitive results in complex situations of overdetermination where two or more potential causes are present. To handle such situations, Wright defined the NESS Test, considered to be a significant refinement of Hart and Honore's classic approach to causality in the law. We show that though Wright's terminology lacks the mathematical rigor of Halpern and Pearl, the Halpern and Pearl definition essentially formalizes Wright's definition, provides an alternative theory of the test's validity, and fixes problems with the NESS test raised by Wright's critics. However, the Halpern and Pearl definition seems to yield puzzling results in some situations involving double omission, and we propose a solution.