Related papers: An $\ell^p$-based Kernel Conditional Independence Test

An $\ell^p$-based Kernel Conditional Independence Test

URL: http://arxiv.org/abs/2110.14868v1
Date: Thu, 28 Oct 2021 03:18:27 GMT
Title: An $\ell^p$-based Kernel Conditional Independence Test
Authors: Meyer Scetbon, Laurent Meunier, Yaniv Romano
Abstract summary: We propose a new computationally efficient test for conditional independence based on the $Lp$ distance between two kernel-based representatives of well suited distributions. We conduct a series of experiments showing that the performance of our new tests outperforms state-of-the-art methods both in term of statistical power and type-I error even in the high dimensional setting.
Score: 21.689461247198388
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose a new computationally efficient test for conditional independence based on the $L^{p}$ distance between two kernel-based representatives of well suited distributions. By evaluating the difference of these two representatives at a finite set of locations, we derive a finite dimensional approximation of the $L^{p}$ metric, obtain its asymptotic distribution under the null hypothesis of conditional independence and design a simple statistical test from it. The test obtained is consistent and computationally efficient. We conduct a series of experiments showing that the performance of our new tests outperforms state-of-the-art methods both in term of statistical power and type-I error even in the high dimensional setting.

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