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Published July 2011 | Accepted Version
Book Section - Chapter Open

Noisy-OR Models with Latent Confounding

Abstract

Given a set of experiments in which varying subsets of observed variables are subject to intervention, we consider the problem of identifiability of causal models exhibiting latent confounding. While identifiability is trivial when each experiment intervenes on a large number of variables, the situation is more complicated when only one or a few variables are subject to intervention per experiment. For linear causal models with latent variables Hyttinen et al. (2010) gave precise conditions for when such data are sufficient to identify the full model. While their result cannot be extended to discrete-valued variables with arbitrary cause-effect relationships, we show that a similar result can be obtained for the class of causal models whose conditional probability distributions are restricted to a 'noisy-OR' parameterization. We further show that identification is preserved under an extension of the model that allows for negative influences, and present learning algorithms that we test for accuracy, scalability and robustness.

Additional Information

© 2011 AUAI Press. We thank three anonymous reviewers for helpful comments. A.H. & P.O.H. were supported by the Academy of Finland (project #1125272) and by University of Helsinki Research Funds (project #490012).

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