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Detection of unfaithfulness and robust causal inference

Zhang, Jiji and Spirtes, Peter (2008) Detection of unfaithfulness and robust causal inference. Minds and Machines, 18 (2). pp. 239-271. ISSN 0924-6495. doi:10.1007/s11023-008-9096-4.

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Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to two methodologically significant observations: (1) some common types of counterexamples to the Faithfulness condition constitute objections only to the empirically testable part of the condition; and (2) some common defenses of the Faithfulness condition do not provide justification or evidence for the testable parts of the condition. It is thus worthwhile to study the possibility of reliable causal inference under weaker Faithfulness conditions. As it turns out, the modification needed to make standard procedures work under a weaker version of the Faithfulness condition also has the practical effect of making them more robust when the standard Faithfulness condition actually holds. This, we argue, is related to the possibility of controlling error probabilities with finite sample size (‘‘uniform consistency’’) in causal inference.

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Additional Information:© Springer Science+Business Media B.V. 2008. Received: 19 April 2007 / Accepted: 2 February 2008 / Published online: 18 March 2008.We thank Clark Glymour, Kevin Kelly, Thomas Richardson, Richard Scheines,Oliver Schulte, and James Woodward for very helpful comments. An earlier draft of this paper was presented to the Confirmation, Induction and Science conference held at the London School of Economics and Political Science in March 2007, and we are grateful to the participates for their useful feedback. Special thanks are due to Joseph Ramsey for providing empirical results on the performance of causal discovery algorithms discussed in the paper.
Subject Keywords:Bayesian network; causal inference; epistemology of causation; faithfulness condition; machine learning;uniform consistency
Issue or Number:2
Record Number:CaltechAUTHORS:ZHAmm08
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13611
Deposited By: Lindsay Cleary
Deposited On:13 May 2009 18:39
Last Modified:08 Nov 2021 22:39

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