Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Probabilistic counterfactuals: semantics, computation, and applications
Probabilistic counterfactuals: semantics, computation, and applications
Artificial Intelligence - Special issue on relevance
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Psychological and normative theories of causal power and the probabilities of causes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Counterfactuals and policy analysis in structural models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Testing identifiability of causal effects
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Decision-theoretic troubleshooting: a framework for repair and experiment
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Reasoning with cause and effect
AI Magazine
Causes and explanations: a structural-model approach: part i: causes
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structural‐semantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to bound these quantities from data obtained in experimental and observational studies, under general assumptions concerning the data‐generating process. In particular, we strengthen the results of Pearl [39] by presenting sharp bounds based on combined experimental and nonexperimental data under no process assumptions, as well as under the mild assumptions of exogeneity (no confounding) and monotonicity (no prevention). These results delineate more precisely the basic assumptions that must be made before statistical measures such as the excess‐risk‐ratio could be used for assessing attributional quantities such as the probability of causation.