David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 121 (1-2):29-54 (1999)
There have been many efforts to infer causation from association byusing statistical models. Algorithms for automating this processare a more recent innovation. In Humphreys and Freedman[(1996) British Journal for the Philosophy of Science 47, 113–123] we showed that one such approach, by Spirtes et al., was fatally flawed. Here we put our arguments in a broader context and reply to Korb and Wallace [(1997) British Journal for thePhilosophy of Science 48, 543–553] and to Spirtes et al.[(1997) British Journal for the Philosophy of Science 48, 555–568]. Their arguments leave our position unchanged: claims to have developed a rigorous engine for inferring causation from association are premature at best, the theorems have no implications for samples of any realistic size, and the examples used to illustrate the algorithms are indicative of failure rather than success. The gap between association and causation has yet to be bridged.
|Keywords||Philosophy Philosophy Epistemology Logic Metaphysics Philosophy of Language|
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Citations of this work BETA
Michael Baumgartner & Isabelle Drouet (2013). Identifying Intervention Variables. European Journal for Philosophy of Science 3 (2):183-205.
Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.
Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):761-768.
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