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  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.
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Tian, J., Pearl, J. Probabilities of causation: Bounds and identification. Annals of Mathematics and Artificial Intelligence 28, 287–313 (2000). https://doi.org/10.1023/A:1018912507879
- Causal Effect
- Causal Model
- Causal Power
- Causal Graph
- Joint Probability Function