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Poster
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Meyer Scetbon · Laurent Meunier · Yaniv Romano
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 Information
Meyer Scetbon (CREST, ENSAE)
Laurent Meunier (Dauphine University - FAIR Paris)
Yaniv Romano (Technion---Israel Institute of Technology)
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2022 Spotlight: An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings »
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