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On the completeness of an identifiability algorithm for semi-Markovian models

On the completeness of an identifiability algorithm for semi-Markovian models This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of non-observable (i.e., hidden or latent) variables. It is well known that the answer to the question depends on the structure of the causal Bayesian network, the set of observable variables, the set of effect variables, and the set of intervention variables. Sound algorithms for identifiability have been proposed, but no complete algorithm is known. We show that the identify algorithm that Tian and Pearl defined for semi-Markovian models (Tian and Pearl 2002, 2002, 2003), an important special case of causal Bayesian networks, is both sound and complete. We believe that this result will prove useful to solve the identifiability question for general causal Bayesian networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

On the completeness of an identifiability algorithm for semi-Markovian models

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References (12)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media B.V.
Subject
Computer Science; Complexity; Mathematics, general; Computer Science, general; Artificial Intelligence (incl. Robotics)
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-008-9101-x
Publisher site
See Article on Publisher Site

Abstract

This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of non-observable (i.e., hidden or latent) variables. It is well known that the answer to the question depends on the structure of the causal Bayesian network, the set of observable variables, the set of effect variables, and the set of intervention variables. Sound algorithms for identifiability have been proposed, but no complete algorithm is known. We show that the identify algorithm that Tian and Pearl defined for semi-Markovian models (Tian and Pearl 2002, 2002, 2003), an important special case of causal Bayesian networks, is both sound and complete. We believe that this result will prove useful to solve the identifiability question for general causal Bayesian networks.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: May 13, 2009

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