Derandomized knockoffs: leveraging e-values for false discovery rate control

Understand

Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR).

• Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice.
• In this paper, we introduce a methodology for derandomizing model-X knockoffs with provable FDR control.
• The key insight of our proposed method lies in the discovery that the knockoffs procedure is in essence an e-BH procedure.