A Bayesian Approach to Causal Discovery

A Bayesian Approach to Causal Discovery (1997)

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by David Heckerman , Christopher Meek , Gregory Cooper

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Datum	Value	Source
TITLE	A Bayesian Approach to Causal Discovery	INFERENCE
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.1
AUTHOR AFFIL	Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052	SVM HeaderParse 0.1
AUTHOR NAME	Christopher Meek	SVM HeaderParse 0.1
AUTHOR AFFIL	Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052	SVM HeaderParse 0.1
AUTHOR NAME	Gregory Cooper	SVM HeaderParse 0.1
AUTHOR AFFIL	; University of Pittsburgh; Microsoft Research; Advanced Technology Division; Microsoft Corporation	SVM HeaderParse 0.2
AUTHOR ADDR	; Pittsburgh, PA; One Microsoft Way; Redmond, WA	SVM HeaderParse 0.1
ABSTRACT	We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraint-based approach. Both approaches rely on the Causal Markov assumption, but the two differ significantly in theory and practice. An important difference between the approaches is that the constraint-based approach uses categorical information about conditional-independence constraints in the domain, whereas the Bayesian approach weighs the degree to which such constraints hold. As a result, the Bayesian approach has three distinct advantages over its constraint-based counterpart. One, conclusions derived from the Bayesian approach are not susceptible to incorrect categorical decisions about independence facts that can occur with data sets of finite size. Two, using the Bayesian approach, finer distinctions among model structures---both quantitative and qualitative---can be made. Three, information from several models can be combined to make better inferences and to better account for modeling uncertainty. In addition to describing the general Bayesian approach to causal discovery, we review approximation methods for missing data and hidden variables, and illustrate differences between the Bayesian and constraint-based methods using artificial and real examples. 1	SVM HeaderParse 0.1
YEAR	1997	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report MSR-TR-97-05, Microsoft Research	INFERENCE
CITATIONS	49 found	ParsCit 1.0