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  Inferring deterministic causal relations

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., et al. (2010). Inferring deterministic causal relations. In P. Grünwald, & P. Spirtes (Eds.), 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) (pp. 143-150). Corvallis, OR, USA: AUAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF3C-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-81CD-5
Genre: Conference Paper

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UAI2010-Daniusis_[0].pdf (Any fulltext), 368KB
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 Creators:
Daniusis, P1, 2, Author              
Janzing, D1, 2, Author              
Mooij, J1, 2, Author              
Zscheischler, J1, 2, Author              
Steudel, B, Author              
Zhang, K1, 2, 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 consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

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 Dates: 2010-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 6620
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Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Place of Event: Catalina Island, CA, USA
Start-/End Date: 2010-07-08 - 2010-07-11

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Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Source Genre: Proceedings
 Creator(s):
Grünwald, P, Editor
Spirtes, P, Editor
Affiliations:
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Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 143 - 150 Identifier: ISBN: 978-0-9749039-6-5