Machine learning approaches to statistical dependences and causality

Janzing, D; Lauritzen, S; Schölkopf, B

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Machine learning approaches to statistical dependences and causality

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Janzing, D
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://drops.dagstuhl.de/opus/volltexte/2010/2363/pdf/09401_abstracts_collection.2363.pdf
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Citation

Janzing, D., Lauritzen, S., & Schölkopf, B. (2009). Machine learning approaches to statistical dependences and causality.

Cite as: https://hdl.handle.net/21.11116/0000-0007-8C1C-D

Abstract

From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistical dependences and causality'' was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.