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Conference Paper

Detecting low-complexity unobserved causes

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Janzing,  D.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Sgouritsa,  E.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Stegle,  O.
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schoelkopf,  B.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

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).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-4C99-2
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.