Uncertainty in Artificial Intelligence
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Testing Identifiability of Causal Effects
David Galles, Judea Pearl
Abstract:
This paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.
Keywords: Identifiability, causal inference, causal networks, interventions.
Pages: 185-195
PS Link: ftp://ftp.cs.ucla.edu/pub/stat_ser/R226-U.ps.Z
PDF Link: /papers/95/p185-galles.pdf
BibTex:
@INPROCEEDINGS{Galles95,
AUTHOR = "David Galles and Judea Pearl",
TITLE = "Testing Identifiability of Causal Effects",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "185--195"
}


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