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  Detecting low-complexity unobserved causes

Janzing, D., Sgouritsa, E., Stegle, O., Peters, J., & Schoelkopf, B. (2011). Detecting low-complexity unobserved causes. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 383-391).

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 Creators:
Janzing, D.1, Author           
Sgouritsa, E.1, Author           
Stegle, O.2, Author           
Peters, J.1, Author           
Schoelkopf, B.1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497664              

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Free keywords: MPI für Intelligente Systeme; Abt. Schölkopf;
 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: 2011-07-01
 Publication Status: Issued
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Title: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Source Genre: Proceedings
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Pages: 8 Volume / Issue: - Sequence Number: - Start / End Page: 383 - 391 Identifier: -