Titlebook: Causation, Prediction, and Search; Peter Spirtes,Clark Glymour,Richard Scheines Book 1993 Springer-Verlag New York, Inc. 1993 Microsoft Ac

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0930-0325 se, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer scien978-1-4612-7650-0978-1-4612-2748-9Series ISSN 0930-0325 Series E-ISSN 2197-7186

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https://doi.org/10.1007/978-3-663-10962-4lies to prediction, at least outside of econometrics. The textbooks consider cases where policy interventions are at issue, but they tell us nothing systematic about the connections between statistical analysis of observations or experiments and predictions of the effects of policies, actions or man

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,Organisation und Geschlechterverhältnis,ful to graph structure; and still another is obtained if the distributions must be consistent with a linear structure, and so on. For each case of interest, the problem is to characterize the indistinguishability classes graph-theoretically, for only then will one have a general understanding of the

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https://doi.org/10.1007/978-3-663-10967-9les, and also to models for discrete variables. Other strategies than those we consider here are also promising; the Bayesian methods of Cooper and Herskovits, in particular, could be adapted to the elaboration problem.

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Discovery Algorithms without Causal Sufficiency,cal studies that claim to show that exposure to a risk factor causes disease, a burden of the argument is to show that the statistical association is not due to some common cause of risk factor and disease; since not everything imaginably relevant can be measured, the argument is radically incomplet

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