A Kernel-Based Test for Multi-Way Conditional Independence

Abstract

Conditional independence involving more than two variables, or multi-way conditional independence, is a fundamental probabilistic structure useful in many areas of statistical research, including latent-variable and graphical models. However, most existing works focus on pairwise conditional independence. To address this gap, we propose a kernel-based test of multi-way conditional independence. Specifically, we characterize this hypothesis through the simultaneous vanishing of a vector of conditional cross-covariance operators and construct a test statistic based on the norm of the vector of their estimators. We derive the statistic's asymptotic distributions under the null hypothesis and local alternatives. As a related methodological contribution, we also develop a test of multi-way unconditional independence and provide a procedure for its implementation. An application to RNA data illustrates the method's use in genomics.