( events)   Timezone: »  
Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #513
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.