Uncertainty in Artificial Intelligence
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Probabilities of Causation: Bounds and Identification
Jin Tian, Judea Pearl
Abstract:
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.
Keywords: causation,causality
Pages: 589-598
PS Link: ftp://ftp.cs.ucla.edu/pub/stat_ser/R271-U.ps
PDF Link: /papers/00/p589-tian.pdf
BibTex:
@INPROCEEDINGS{Tian00,
AUTHOR = "Jin Tian and Judea Pearl",
TITLE = "Probabilities of Causation: Bounds and Identification",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "589--598"
}


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