Are there algorithms that discover causal structure?
Synthese 121 (1-2):29-54 (1999)
| Abstract | 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. | |||||||||
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Similar books and articlesGurol Irzik (1986). Causal Modeling and the Statistical Analysis of Causation. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:12 - 23.
Paul Humphreys (1980). Probabilistic Causality and Multiple Causation. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980:25 - 37.
D. Fallis (2000). The Reliability of Randomized Algorithms. British Journal for the Philosophy of Science 51 (2):255-271.
Kevin B. Korb & Chris S. Wallace (1997). In Search of the Philosopher's Stone: Remarks on Humphreys and Freedman's Critique of Causal Discovery. British Journal for the Philosophy of Science 48 (4):543-553.
Peter Spirtes, Clark Glymour & Richard Scheines (1997). Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search. British Journal for the Philosophy of Science 48 (4):555-568.
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