Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
نویسندگان
چکیده
منابع مشابه
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery. Due to the curse of dimensionality the case 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...
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ژورنال
عنوان ژورنال: Journal of Causal Inference
سال: 2018
ISSN: 2193-3685,2193-3677
DOI: 10.1515/jci-2018-0017