Practical Kernel Tests of Conditional Independence

arXiv:2402.13196v1 Announce Type: new
Abstract: We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing, absent in tests of unconditional independence, is to obtain the correct test level (the specified upper bound on the rate of false positives), while still attaining competitive test power. Excess false positives arise due to bias in the test statistic, which is obtained using nonparametric kernel ridge regression. We propose three methods for bias control to …

abstract arxiv challenge cs.lg data false false positives kernel major power practical rate statistical test testing tests type