Papers › Conditional Independence Testing using Generative Adversarial Networks

Conditional Independence Testing using Generative Adversarial Networks

9 Jul 2019NeurIPS 2019 12arXiv:1907.04068archive 2025-07-28

Alexis Bellot, Mihaela van der Schaar

We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces. Our contribution is a new test statistic based on samples from a generative adversarial network designed to approximate directly a conditional distribution that encodes the null hypothesis, in a manner that maximizes power (the rate of true negatives). We show that such an approach requires only that density approximation be viable in order to ensure that we control type I error (the rate of false positives); in particular, no assumptions need to be made on the form of the distributions or feature dependencies. Using synthetic simulations with high-dimensional data we demonstrate significant gains in power over competing methods. In addition, we illustrate the use of our test to discover causal markers of disease in genetic data.

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