Timezone: »

 
Spotlight
Conditional Independence Testing using Generative Adversarial Networks
Alexis Bellot · Mihaela van der Schaar

Tue Dec 10 10:40 AM -- 10:45 AM (PST) @ West Ballroom A + B
in Track 3 Session 1 »

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.

[ Paper]  [ Poster]  [ Slides] 

Author Information

Alexis Bellot (University of Cambridge / Alan Turing Institute)
Mihaela van der Schaar (University of Cambridge, Alan Turing Institute and UCLA)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors