Knockoffs with Side Information

Understand

We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available.

• In principle, using side information can allow the statistician to pay attention to variables with a greater potential, which in turn, may lead to more discoveries.
• We introduce an adaptive knockoff filter, which generalizes the knockoff procedure (Barber and Cand\`es, 2015; Cand\`es et al., 2018) in that it uses both the data at hand and side information to adaptively order the variables under study and focus on those that are most promising.
• Adaptive knockoffs controls the finite-sample false discovery rate (FDR) and we demonstrate its power by comparing it with other structured multiple testing methods.