Uncertainty in Artificial Intelligence
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A Bayesian Approach to Learning Causal Networks
David Heckerman
Abstract:
Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called {em mechanism independence} and {em component independence}. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
Keywords: Causality, Bayesian networks, causal networks, influence diagrams, canonical form, l
Pages: 285-295
PS Link: http://www.research.microsoft.com/research/dtg/heckerma/TR-95-04.htm
PDF Link: /papers/95/p285-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman95,
AUTHOR = "David Heckerman ",
TITLE = "A Bayesian Approach to Learning Causal Networks",
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 = "285--295"
}


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