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Are There Algorithms That Discover Causal Structure?

David Freedman and Paul Humphreys
Synthese
Vol. 121, No. 1/2, Statistics and Causation (1999), pp. 29-54
Published by: Springer
Stable URL: http://www.jstor.org/stable/20118220
Page Count: 26
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Are There Algorithms That Discover Causal Structure?
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Abstract

There have been many efforts to infer causation from association by using statistical models. Algorithms for automating this process are 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 the Philosophy 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.

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