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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: 596814.0, MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung) / Abt. Schölkopf (Empirical Inference)
Kernel-based Conditional Independence Test and Application in Causal Discovery
Authors:Zhang, K.; Peters, J.; Janzing, D.; Schölkopf, B.
Publisher:AUAI Press
Place of Publication:Corvallis, OR
Date of Publication (YYYY-MM-DD):2011-07-01
Title of Proceedings:27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Start Page:804
End Page:813
Physical Description:9
Place of Conference/Meeting:Barcelona, Spain
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery. Due to the curse of dimensionality, testing for conditional independence of continuous variables is particularly challenging. We propose a Kernel-based Conditional Independence test (KCI-test), by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional
independence. The proposed method is computationally efficient and easy to implement. Experimental results show that it outperforms other methods, especially when the conditioning set is large or the sample size is not very large, in which case other methods encounter difficulties.
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:ZhangPJS2011
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