Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf
Abstract:
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.
Keywords:
Pages: 804-813
PS Link:
PDF Link: /papers/11/p804-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang11,
AUTHOR = "Kun Zhang and Jonas Peters and Dominik Janzing and Bernhard Schoelkopf",
TITLE = "Kernel-based Conditional Independence Test and Application in Causal Discovery",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "804--813"
}


hosted by DSL   •   site info   •   help