Fast and powerful conditional randomization testing via distillation.

ABSTRACT: We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y⫫X∣Z. The conditional randomization test was recently proposed as a way to use distributional information about X∣Z to exactly and nonasymptotically control Type-I error using any test statistic in any dimensionality without assuming anything about Y∣(X,Z). This flexibility, in principle, allows one to derive powerful test statistics from complex prediction algorithms while maintaining statistical validity. Yet the direct use of such advanced test statistics in the conditional randomization test is prohibitively computationally expensive, especially with multiple testing, due to the requirement to recompute the test statistic many times on resampled data.

SUBMITTER: Liu M 

PROVIDER: S-EPMC10323874 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature