  This paper introduces an innovative method for conducting conditional
independence testing in high-dimensional data, facilitating the automated
discovery of significant associations within distinct subgroups of a
population, all while controlling the false discovery rate. This is achieved by
expanding upon the model-X knockoff filter to provide more informative
inferences. Our enhanced inferences can help explain sample heterogeneity and
uncover interactions, making better use of the capabilities offered by modern
machine learning models. Specifically, our method is able to leverage any model
for the identification of data-driven hypotheses pertaining to interesting
population subgroups. Then, it rigorously test these hypotheses without
succumbing to selection bias. Importantly, our approach is efficient and does
not require sample splitting. We demonstrate the effectiveness of our method
through simulations and numerical experiments, using data derived from a
randomized experiment featuring multiple treatment variables.
