Detection of Unfaithfulness and Robust Causal Inference
 Jiji Zhang,
 Peter Spirtes
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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.
Inside
Within this Article
 Introduction
 Causal Graph and Causal Inference
 A Decomposition of CFC
 A Further Characterization of Undetectable Failure of Faithfulness
 More Robust Causal Inference with a Check of Unfaithfulness
 Conclusion
 References
 References
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 Title
 Detection of Unfaithfulness and Robust Causal Inference
 Journal

Minds and Machines
Volume 18, Issue 2 , pp 239271
 Cover Date
 20080601
 DOI
 10.1007/s1102300890964
 Print ISSN
 09246495
 Online ISSN
 15728641
 Publisher
 Springer Netherlands
 Additional Links
 Topics
 Keywords

 Bayesian network
 Causal inference
 Epistemology of causation
 Faithfulness condition
 Machine learning
 Uniform consistency
 Industry Sectors
 Authors

 Jiji Zhang ^{(1)}
 Peter Spirtes ^{(2)}
 Author Affiliations

 1. Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
 2. Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, 15213, USA