Title
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
Document Type
Journal article
Source Publication
Artificial Intelligence
Publication Date
11-1-2008
Volume
172
Issue
16–17
First Page
1873
Last Page
1896
Publisher
Elsevier BV
Keywords
Ancestral graphs, Automated causal discovery, Bayesian networks, Causal models, Markov equivalence, Latent variables
Abstract
Causal discovery becomes especially challenging when the possibility of latent confounding and/or selection bias is not assumed away. For this task, ancestral graph models are particularly useful in that they can represent the presence of latent confounding and selection effect, without explicitly invoking unobserved variables. Based on the machinery of ancestral graphs, there is a provably sound causal discovery algorithm, known as the FCI algorithm, that allows the possibility of latent confounders and selection bias. However, the orientation rules used in the algorithm are not complete. In this paper, we provide additional orientation rules, augmented by which the FCI algorithm is shown to be complete, in the sense that it can, under standard assumptions, discover all aspects of the causal structure that are uniquely determined by facts of probabilistic dependence and independence. The result is useful for developing any causal discovery and reasoning system based on ancestral graph models.
DOI
10.1016/j.artint.2008.08.001
Print ISSN
00043702
E-ISSN
18727921
Publisher Statement
Copyright © 2008 Elsevier B.V.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172(0), 1873-1896. doi: 10.1016/j.artint.2008.08.001
