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  Identifying Cause and Effect on Discrete Data using Additive Noise Models

Peters, J., Janzing, D., & Schölkopf, B. (2010). Identifying Cause and Effect on Discrete Data using Additive Noise Models. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 597-604.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C040-6 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C041-4
Genre: Konferenzbeitrag

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 Urheber:
Peters, J1, Autor           
Janzing, D2, Autor           
Schölkopf, B1, Autor           
Teh M. Titterington, Y.W., 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: Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work we extend the notion of additive noise models to these cases. Whenever the joint distribution P(X;Y ) admits such a model in one direction, e.g. Y = f(X) + N; N ? X, it does not admit the reversed model X = g(Y ) + ~N ; ~N ? Y as long as the model is chosen in a generic way. Based on these deliberations we propose an efficient new algorithm that is able to distinguish between cause and effect for a finite sample of discrete variables. We show that this algorithm works both on synthetic and real data sets.

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 Datum: 2010-05
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://www.aistats.org/aistats2010/
BibTex Citekey: 6387
 Art des Abschluß: -

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Titel: Thirteenth International Conference on Artificial Intelligence and Statistics
Veranstaltungsort: Chia Laguna Resort, Italy
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Titel: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Cambridge, MA, USA : JMLR
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 597 - 604 Identifikator: -