  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.
