Uncertainty in Artificial Intelligence
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Testing Identifiability of Causal Effects
David Galles, Judea Pearl
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
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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