An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19328-19346, 2022.
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.