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          Institute: MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung)     Collection: Abt. Schölkopf (Empirical Inference)     Display Documents



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ID: 596812.0, MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung) / Abt. Schölkopf (Empirical Inference)
Detecting low-complexity unobserved causes
Authors:Janzing, D.; Sgouritsa, E.; Stegle, O.; Peters, J.; Schölkopf, B.
Date of Publication (YYYY-MM-DD):2011-07-01
Title of Proceedings:27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Start Page:383
End Page:391
Physical Description:8
Place of Conference/Meeting:Barcelona, Spain
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Heide Klooz
Affiliations:MPI für Intelligente Systeme/Abt. Schölkopf
Identifiers:URL:http://www.kyb.tuebingen.mpg.de/fileadmin/user_upl...
LOCALID:JanzingSSPS2011
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