Learning to Increase the Power of Conditional Randomization Tests. (arXiv:2207.01022v1 [cs.LG])

The model-X conditional randomization test is a generic framework for
conditional independence testing, unlocking new possibilities to discover
features that are conditionally associated with a response of interest while
controlling type-I error rates. An appealing advantage of this test is that it
can work with any machine learning model to design powerful test statistics. In
turn, the common practice in the model-X literature is to form a test statistic
using machine learning models, trained to maximize predictive accuracy with the …

arxiv learning lg power randomization tests