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Statistical Indistinguishability

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Causation, Prediction, and Search

Part of the book series: Lecture Notes in Statistics ((LNS,volume 81))

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Abstract

Without experimental manipulations, the resolving power of any possible method for inferring causal structure from statistical relationships is limited by statistical indistinguishability. If two causal structures can equally account for the same statistics, then no statistics can distinguish them. The notions of statistical indistinguishability for causal hypotheses vary with the restrictions one imposes on the connections between directed graphs representing causal structure and probabilities representing the associated joint distribution of the variables. If one requires only that the Markov and Minimality Conditions be satisfied, then two causal graphs will be indistinguishable if the same class of distributions satisfy those conditions for one of the graphs as for the other. A different statistical indistinguishability relation is obtained if one requires that distributions be faithful 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 causal structures that cannot be distinguished under the general assumptions connecting causal graphs and distributions.

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© 1993 Springer-Verlag New York, Inc.

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Spirtes, P., Glymour, C., Scheines, R. (1993). Statistical Indistinguishability. In: Causation, Prediction, and Search. Lecture Notes in Statistics, vol 81. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2748-9_4

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  • DOI: https://doi.org/10.1007/978-1-4612-2748-9_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7650-0

  • Online ISBN: 978-1-4612-2748-9

  • eBook Packages: Springer Book Archive

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