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On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias

Zhang, Jiji (2008) On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172 (16-17). pp. 1873-1896. ISSN 0004-3702. https://resolver.caltech.edu/CaltechAUTHORS:ZHAai08

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.artint.2008.08.001DOIUNSPECIFIED
Additional Information:Copyright © 2008 Elsevier. Received 28 October 2007; revised 30 June 2008; accepted 6 August 2008. Available online 14 August 2008. I thank Peter Spirtes and Thomas Richardson for many suggestions and especially for their time in checking the proofs. I am also grateful to the referees for useful comments.
Subject Keywords:Ancestral graphs; Automated causal discovery; Bayesian networks; Causal models; Markov equivalence; Latent variables
Issue or Number:16-17
Record Number:CaltechAUTHORS:ZHAai08
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:ZHAai08
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:12648
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:17 Dec 2008 00:06
Last Modified:03 Oct 2019 00:30

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