David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Minds and Machines 18 (2):239-271 (2008)
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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References found in this work BETA
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
James Woodward (2003). Making Things Happen: A Theory of Causal Explanation. Oxford University Press.
Nancy Cartwright (1999). The Dappled World: A Study of the Boundaries of Science. Cambridge University Press.
Nancy Cartwright (1989). Nature's Capacities and Their Measurement. Oxford University Press.
Clark Glymour (1980). Theory and Evidence. Princeton University Press.
Citations of this work BETA
Gerhard Schurz & Alexander Gebharter (forthcoming). Causality as a Theoretical Concept: Explanatory Warrant and Empirical Content of the Theory of Causal Nets. Synthese:1-31.
Peter Gildenhuys (2010). Causal Equations Without Ceteris Paribus Clauses. Philosophy of Science 77 (4):608-632.
Holly Andersen (2013). When to Expect Violations of Causal Faithfulness and Why It Matters. Philosophy of Science (5):672-683.
Clark Glymour (2010). What is Right with 'Bayes Net Methods' and What is Wrong with 'Hunting Causes and Using Them'? British Journal for the Philosophy of Science 61 (1):161-211.
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Daniel M. Hausman & James Woodward (2004). Modularity and the Causal Markov Condition: A Restatement. British Journal for the Philosophy of Science 55 (1):147-161.
DM Hausman & J. Woodward (1999). Independence, Invariance and the Causal Markov Condition. British Journal for the Philosophy of Science 50 (4):521-583.
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