Conditional Randomization Rank Test

TL;DR

The paper introduces the Conditional Randomization Rank Test (CRRT), a new method for testing conditional independence that improves computational efficiency and error control over previous methods by leveraging knowledge of the conditional distribution.

Contribution

The paper presents CRRT, a novel conditional independence testing method that generalizes CRT, offering better computational efficiency and error guarantees under distribution misspecification.

Findings

CRRT guarantees exact type 1 error control.

CRRT outperforms CRT in computational efficiency.

Extensive simulations validate the theoretical advantages of CRRT.

Abstract

We propose a new method named the Conditional Randomization Rank Test (CRRT) for testing conditional independence of a response variable Y and a covariate variable X, conditional on the rest of the covariates Z. The new method generalizes the Conditional Randomization Test (CRT) of [CFJL18] by exploiting the knowledge of the conditional distribution of X|Z and is a conditional sampling based method that is easy to implement and interpret. In addition to guaranteeing exact type 1 error control, owing to a more flexible framework, the new method markedly outperforms the CRT in computational efficiency. We establish bounds on the probability of type 1 error in terms of total variation norm and also in terms of observed Kullback-Leibler divergence when the conditional distribution of X|Z is misspecified. We validate our theoretical results by extensive simulations and show that our new method has considerable advantages over other existing conditional sampling based methods when we take both power and computational efficiency into consideration.