Uncertainty in Artificial Intelligence
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An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
Tom Verma, Judea Pearl
Abstract:
In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.
Keywords:
Pages: 323-330
PS Link:
PDF Link: /papers/92/p323-verma.pdf
BibTex:
@INPROCEEDINGS{Verma92,
AUTHOR = "Tom Verma and Judea Pearl",
TITLE = "An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "323--330"
}


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