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

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.

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Full-text Version

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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