An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

Meyer Scetbon; Laurent Meunier; Yaniv Romano

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

Meyer Scetbon, Laurent Meunier, Yaniv Romano

Research output: Contribution to journal › Conference article › peer-review

Abstract

We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.

Original language	English
Pages (from-to)	19328-19346
Number of pages	19
Journal	Proceedings of Machine Learning Research
Volume	162
State	Published - 2022
Externally published	Yes
Event	39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States Duration: 17 Jul 2022 → 23 Jul 2022 https://proceedings.mlr.press/v162/

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software
Control and Systems Engineering
Statistics and Probability

Cite this

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title = "An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings",

abstract = "We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.",

author = "Meyer Scetbon and Laurent Meunier and Yaniv Romano",

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TY - JOUR

T1 - An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

AU - Scetbon, Meyer

AU - Meunier, Laurent

AU - Romano, Yaniv

PY - 2022

Y1 - 2022

N2 - We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.

AB - We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.

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M3 - مقالة من مؤنمر

SN - 2640-3498

VL - 162

SP - 19328

EP - 19346

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

T2 - 39th International Conference on Machine Learning, ICML 2022

Y2 - 17 July 2022 through 23 July 2022

ER -

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

Abstract

All Science Journal Classification (ASJC) codes

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