Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values

We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of natural