Reviews: Model-Powered Conditional Independence Test

This paper proposed a model powered approach to conduct conditional independent tests for iid data. The basic idea is to use nearest neighbor bootstrap to generate samples which follow a distribution close to the f {CI} and a classifier is trained and tested to see if it is able to distinguish the observation distribution and the nearest neighbor bootstrapped distribution. If the classification performance is close to the random guess, one fails to reject the null hypothesis that data follows conditional independence otherwise one accept the null hypothesis. In general, the paper is trying to address an important problem and the paper is presented in a clear way. It seems that the whole method can be decoupled into two major components.