Probabilities of Causation: Bounds and Identification
It addresses attribution problems in decision-making, strengthening prior results by weakening assumptions, but is incremental in nature.
The paper tackles the problem of estimating the probability that one event caused another, deriving theoretically sharp bounds for probabilities of necessary or sufficient causation from experimental and observational data with minimal assumptions.
This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structural-semantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to optimally bound these quantities from data obtained in experimental and observational studies, making minimal assumptions concerning the data-generating process. In particular, we strengthen the results of Pearl (1999) by weakening the data-generation assumptions and deriving theoretically sharp bounds on the probabilities of causation. These results delineate precisely how empirical data can be used both in settling questions of attribution and in solving attribution-related problems of decision making.