The Hardness of Conditional Independence Testing and the Generalised Covariance Measure

TL;DR

This paper demonstrates the inherent difficulty of conditional independence testing, proves the non-existence of a universally powerful test, and proposes a practical, regression-based method called the Generalised Covariance Measure (GCM) for univariate and high-dimensional data.

Contribution

It introduces the GCM method for conditional independence testing, showing its validity relies on slow-rate regression estimates and extending it to high-dimensional settings.

Findings

GCM is competitive with state-of-the-art tests in simulations.

Validity depends mainly on regression estimation quality.

Theoretical guarantees are provided for kernel ridge regression.

Abstract

It is a common saying that testing for conditional independence, i.e., testing whether whether two random vectors $X$ and $Y$ are independent, given $Z$, is a hard statistical problem if $Z$ is a continuous random variable (or vector). In this paper, we prove that conditional independence is indeed a particularly difficult hypothesis to test for. Valid statistical tests are required to have a size that is smaller than a predefined significance level, and different tests usually have power against a different class of alternatives. We prove that a valid test for conditional independence does not have power against any alternative. Given the non-existence of a uniformly valid conditional independence test, we argue that tests must be designed so their suitability for a particular problem may be judged easily. To address this need, we propose in the case where $X$ and $Y$ are univariate to nonlinearly regress $X$ on $Z$, and $Y$ on $Z$ and then compute a test statistic based on the sample covariance between the residuals, which we call the generalised covariance measure (GCM). We prove that validity of this form of test relies almost entirely on the weak requirement that the regression procedures are able to estimate the conditional means $X$ given $Z$, and $Y$ given $Z$, at a slow rate. We extend the methodology to handle settings where $X$ and $Y$ may be multivariate or even high-dimensional. While our general procedure can be tailored to the setting at hand by combining it with any regression technique, we develop the theoretical guarantees for kernel ridge regression. A simulation study shows that the test based on GCM is competitive with state of the art conditional independence tests. Code is available as the R package GeneralisedCovarianceMeasure on CRAN.