A Bayesian approach to learning causal networks

A Bayesian approach to learning causal networks (1995)

by David Heckerman

Venue:	In Uncertainty in AI: Proceedings of the Eleventh Conference
Citations:	52 - 9 self

Datum	Value	Source
TITLE	A Bayesian Approach to Learning Causal Networks	SVM HeaderParse 0.2
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research, Bldg 9S/1	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond 98052-6399, WA	SVM HeaderParse 0.2
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 mechanism independence and 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. 1	SVM HeaderParse 0.2
CITATIONS	24 found	ParsCit 1.0