David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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British Journal for the Philosophy of Science 56 (1):3-26 (2005)
The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. In light of this result, I consider several arguments for supposing indeterminism a particularly hostile environment for the CMC, but conclude that none are persuasive. Introduction Functional models and directed graphs The causal Markov theorem The causal Markov theorem and genuine indeterminism Are the exogenous variables independent? EPR Conclusion.
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Citations of this work BETA
Frederick Eberhardt & Richard Scheines (2007). Interventions and Causal Inference. Philosophy of Science 74 (5):981-995.
Jim Bogen (2008). Causally Productive Activities. Studies in History and Philosophy of Science Part A 39 (1):112-123.
Conor Mayo-Wilson (2014). The Limits of Piecemeal Causal Inference. British Journal for the Philosophy of Science 65 (2):213-249.
Jaakko Kuorikoski (2012). Mechanisms, Modularity and Constitutive Explanation. Erkenntnis 77 (3):361-380.
Frederick Eberhardt (2009). Introduction to the Epistemology of Causation. Philosophy Compass 4 (6):913-925.
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