This article introduces a machine for sampling approximate model-X knockoffs for arbitrary
and unspecified data distributions using deep generative models. The main idea is to …

Abstract Model-X knockoffs is a wrapper that transforms essentially any feature importance
measure into a variable selection algorithm, which discovers true effects while rigorously …

We propose a deep learning–based knockoffs inference framework, DeepLINK, that
guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is …

In this article we develop a method based on model-X knockoffs to find conditional
associations that are consistent across environments, while controlling the false discovery …

Hard thresholding rule is commonly adopted in feature screening procedures to screen out
unimportant predictors for ultrahigh-dimensional data. However, different thresholds are …

Characterizing the asymptotic distributions of eigenvectors for large random matrices poses
important challenges yet can provide useful insights into a range of statistical applications …

We investigate the robustness of the model-X knockoffs framework with respect to the
misspecified or estimated feature distribution. We achieve such a goal by theoretically …

Reconciling model-X and doubly robust approaches to conditional independence testing Page
1 The Annals of Statistics 2024, Vol. 52, No. 3, 895–921 https://doi.org/10.1214/24-AOS2372 © …

Mediation analysis draws increasing attention in many research areas such as economics,
finance and social sciences. In this paper, we propose new statistical inference procedures …

In many scientific applications, researchers aim to relate a response variable to a set of
potential explanatory variables, and start by trying to identify variables that contribute to this …