The conditional randomization test (CRT) was recently proposed to test
whether two random variables X and Y are conditionally independent given random
variables Z. The CRT assumes that the conditional distribution of X given Z is
known under the null hypothesis and then it is compared to the distribution of
the observed samples of the original data. The aim of this paper is to develop
a novel alternative of CRT by using nearest-neighbor sampling without assuming
the exact form of the distribution of X given Z. Specifically, we utilize the
computationally efficient 1-nearest-neighbor to approximate the conditional
distribution that encodes the null hypothesis. Then, theoretically, we show
that the distribution of the generated samples is very close to the true
conditional distribution in terms of total variation distance. Furthermore, we
take the classifier-based conditional mutual information estimator as our test
statistic. The test statistic as an empirical fundamental information theoretic
quantity is able to well capture the conditional-dependence feature. We show
that our proposed test is computationally very fast, while controlling type I
and II errors quite well. Finally, we demonstrate the efficiency of our
proposed test in both synthetic and real data analyses.Comment: Accepted at AAAI 2023; 9 Pages, 3 Figures, 2 Table