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We consider the variable selection problem, which seeks to identify important variables influencing a response Y out of many candidate features X₁, . . . , X_p. We wish to do so while offering finite-sample guarantees about the fraction of false positives—selected variables X_j that in fact have no effect on Y after the other features are known. When the number of features p is large (perhaps even larger than the sample size n), and we have no prior knowledge regarding the type of dependence between Y and X, the model-X knockoffs framework nonetheless allows us to select a model with a guaranteed bound on the false discovery rate, as long as the distribution of the feature vector X = (X₁, . . . , X_p) is exactly known. This model selection procedure operates by constructing “knockoff copies” of each of the p features, which are then used as a control group to ensure that the model selection algorithm is not choosing …