doi
10.1007/3-540-33486-6_1
2006-01-01
We examine the Bayesian approach to the discovery of causal DAG models and compare it to the constraint-based approach. Both approaches rely on the Causal Markov condition, 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 structuresâ€”both quantitative and qualitativeâ€”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.
https://scigraph.springernature.com/explorer/license/
true
2019-04-16T00:46
en
chapter
1-28
chapters
http://link.springer.com/10.1007/3-540-33486-6_1
A Bayesian Approach to Causal Discovery
2006
Heckerman
David
Statistics
Lakhmi C.
Jain
University of Pittsburgh, Pittsburgh, PA
University of Pittsburgh
Innovations in Machine Learning
3-540-30609-9
Holmes
Dawn E.
Cooper
Gregory
Springer Nature - SN SciGraph project
dimensions_id
pub.1008717306
Microsoft Research, Redmond, WA, 98052-6399
Microsoft (United States)
298824b34ffaa2f2ebaa0006656a8b49dd2b1a68872f1a7e0e5084730bb3f6f2
readcube_id
Meek
Christopher
Berlin/Heidelberg
Springer-Verlag
Mathematical Sciences