Probabilities of causation: Bounds and identification

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.

• Publication year

  2000

• Venue

  Annals of Mathematics and Artificial Intelligence

• Publication date

  2000-06-30

• Fields of study

  Mathematics, Philosophy, Computer Science

• Identifiers
  DOI 10.1023/A:1018912507879arXiv 1301.3898
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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