Uncertainty in Artificial Intelligence
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Detecting low-complexity unobserved causes
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schoelkopf
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|x) in the simplex of all distributions of Y. We report encouraging results on semi-empirical data.
Pages: 383-391
PS Link:
PDF Link: /papers/11/p383-janzing.pdf
AUTHOR = "Dominik Janzing and Eleni Sgouritsa and Oliver Stegle and Jonas Peters and Bernhard Schoelkopf",
TITLE = "Detecting low-complexity unobserved causes",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "383--391"

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