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  Telling cause from effect based on high-dimensional observations

Janzing, D., Hoyer, P., & Schölkopf, B. (2010). Telling cause from effect based on high-dimensional observations. In J. Fürnkranz, & T. Joachims (Eds.), 27th International Conference on Machine Learning (ICML 2010) (pp. 479-486). Madison, WI, USA: Omnipress.

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 Creators:
Janzing, D1, 2, Author              
Hoyer, P, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.

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 Dates: 2010-06
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 6501
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Title: 27th International Conference on Machine Learning (ICML 2010)
Place of Event: Haifa, Israel
Start-/End Date: 2010-06-21 - 2010-06-24

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Title: 27th International Conference on Machine Learning (ICML 2010)
Source Genre: Proceedings
 Creator(s):
Fürnkranz, J, Editor
Joachims, T, Editor
Affiliations:
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Publ. Info: Madison, WI, USA : Omnipress
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 479 - 486 Identifier: ISBN: 978-1-605-58907-7