Detection of unfaithfulness and robust causal inference

Minds and Machines 18 (2):239-271 (2008)
Abstract
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.
Keywords Bayesian network  Causal inference  Epistemology of causation  Faithfulness condition  Machine learning  Uniform consistency
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Reprint years 2008
DOI 10.1007/s11023-008-9096-4
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References found in this work BETA

Causality: Models, Reasoning, and Inference.Judea Pearl - 2000 - Cambridge University Press.
Theory and Evidence.Clark Glymour - 1980 - Princeton University Press.

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The Frugal Inference of Causal Relations.Malcolm Forster, Garvesh Raskutti, Reuben Stern & Naftali Weinberger - 2018 - British Journal for the Philosophy of Science 69 (3):821-848.
A Comparison of Three Occam’s Razors for Markovian Causal Models.Jiji Zhang - 2013 - British Journal for the Philosophy of Science 64 (2):423-448.

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