Speaker: Matey Neykov

Abstract: Conditional independence testing (i.e. testing whether X is independent of Y given Z) is an important topic in several fields in statistics such as graphical models and causal inference. It was recently shown that conditional independence testing is “hard” in the continuous Z case. In this paper, we demonstrate that by narrowing down the sets of distributions under the null and alternative hypothesis, conditional independence testing becomes possible. We obtain minimax lower bounds, and develop tests that match the lower bounds under the simplifying assumption of Poissonization. This is joint work with Siva and Larry.