Uncertainty in Artificial Intelligence
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The Recovery of Causal Poly-Trees From Statistical Data
George Rebane, Judea Pearl
Abstract:
Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering poly-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a poly-tree then the topological structure of such tree cam be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.
Keywords: Poly-trees, Casual Networks
Pages: 175-182
PS Link:
PDF Link: /papers/87/p175-rebane.pdf
BibTex:
@INPROCEEDINGS{Rebane87,
AUTHOR = "George Rebane and Judea Pearl",
TITLE = "The Recovery of Causal Poly-Trees From Statistical Data",
BOOKTITLE = "Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1987",
PAGES = "175--182"
}


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