Uncertainty in Artificial Intelligence
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Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf
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.
Pages: 804-813
PS Link:
PDF Link: /papers/11/p804-zhang.pdf
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)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "804--813"

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