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  Detecting low-complexity unobserved causes

Janzing, D., Sgouritsa, E., Stegle, O., Peters, J., & Schölkopf, B. (2011). Detecting low-complexity unobserved causes. In F. Cozman, & A. Pfeffer (Eds.), 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 383-391). Corvallis, OR, USA: AUAI Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB1C-E Version Permalink: https://hdl.handle.net/21.11116/0000-0002-06E9-1
Genre: Conference Paper

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http://auai.org/uai2011/accepted.html (Table of contents)
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https://arxiv.org/abs/1202.3737 (Any fulltext)
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 Creators:
Janzing, D1, 2, Author           
Sgouritsa, E1, 2, Author           
Stegle, O1, 2, 3, Author           
Peters, J1, 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, ou_1497794              
3Max Planck Institute for Developmental Biology, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, DE, ou_2421691              

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 Abstract: We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a \direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y jx) in the simplex of all distributions of Y . We report encouraging results on semi-empirical data.

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 Dates: 2011-07
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: JanzingSSPS2011
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Place of Event: Barcelona, Spain
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Source Genre: Proceedings
 Creator(s):
Cozman, FG, Editor
Pfeffer, A, Editor
Affiliations:
-
Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 383 - 391 Identifier: ISBN: 978-0-9749039-7-2