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  Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions

Sun, X., Janzing, D., & Schölkopf, B. (2007). Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), 441-446.

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 Urheber:
Sun, X1, Autor           
Janzing, D2, Autor           
Schölkopf, B1, Autor           
Verleysen, M., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order.

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 Datum: 2007-04
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://www.dice.ucl.ac.be/esann/proceedings/papers.php?ann=2007
BibTex Citekey: 4454
 Art des Abschluß: -

Veranstaltung

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Titel: 15th European Symposium on Artificial Neural Networks
Veranstaltungsort: Brugge, Belgium
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Titel: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Evere, Belgium : D-Side Publications
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 441 - 446 Identifikator: -