mlsanityData to 7 Oct 2026

Controlling the false discovery rate via knockoffs

arXiv 1404.5609 ↗First posted 22 Apr 2014stat.ME math.ST stat.THcited by 97

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Abstract (arXiv)

In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to discover which variables are truly associated with the response. At the same time, we need to know that the false discovery rate (FDR) - the expected fraction of false discoveries among all discoveries - is not too high, in order to assure the scientist that most of the discoveries are indeed true and replicable. This paper introduces the knockoff filter, a new variable selection procedure controlling the FDR in the statistical linear model whenever there are at least as many observations as variables. This method achieves exact FDR control in finite sample settings no matter the design or covariates, the number of variables in the model, or the amplitudes of the unknown regression coefficients, and does not require any knowledge of the noise level. As the name suggests, the method operates by manufacturing knockoff variables that are cheap - their construction does not require any new data - and are designed to mimic the correlation structure found within the existing variables, in a way that allows for accurate FDR control, beyond what is possible with permutation-based methods. The method of knockoffs is very general and flexible, and can work with a broad class of test statistics. We test the method in combination with statistics from the Lasso for sparse regression, and obtain empirical results showing that the resulting method has far more power than existing selection rules when the proportion of null variables is high.

Key facts. Cited by 97 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2014.

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Citing papers per year: 2021: 15; 2022: 18; 2023: 14; 2024: 18; 2025: 15; 2026: 17.

97 papers.

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  1. 2023Feature Importance: A Closer Look at Shapley Values and LOCOcited by 23
  2. 2023Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discoverycited by 15
  3. 2023Derandomized Novelty Detection with FDR Control via Conformal E-valuescited by 15
  4. 2022Model-X Sequential Testing for Conditional Independence via Testing by Bettingcited by 14
  5. 2024Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agentcited by 14
  6. 2021Data fission: splitting a single data pointcited by 13
  7. 2022Composite Feature Selection using Deep Ensemblescited by 11
  8. 2021The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Controlcited by 10
  9. 2023Conformal link prediction for false discovery rate controlcited by 9
  10. 2024Task-Agnostic Machine-Learning-Assisted Inferencecited by 8
  11. 2021Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learningcited by 7
  12. 2023Sequential Knockoffs for Variable Selection in Reinforcement Learningcited by 7
38 more
  1. 2023Adaptive debiased machine learning using data-driven model selection techniquescited by 7
  2. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  3. 2024FDR-Controlled Portfolio Optimization for Sparse Financial Index Trackingcited by 5
  4. 2021Counterfactual Generation with Knockoffscited by 4
  5. 2021Deep neural networks with controlled variable selection for the identification of putative causal genetic variantscited by 4
  6. 2021Multivariate rank via entropic optimal transport: sample efficiency and generative modelingcited by 4
  7. 2022Causal Discovery using Model Invariance through Knockoff Interventionscited by 4
  8. 2024Sparse PCA with False Discovery Rate Controlled Variable Selectioncited by 4
  9. 2021Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactualscited by 3
  10. 2022Ensembling improves stability and power of feature selection for deep learning modelscited by 3
  11. 2022Near-optimal multiple testing in Bayesian linear models with finite-sample FDR controlcited by 3
  12. 2024High-Dimensional False Discovery Rate Control for Dependent Variablescited by 3
  13. 2024Indirectly Parameterized Concrete Autoencoderscited by 3
  14. 2024Deep Nonparametric Conditional Independence Tests for Imagescited by 3
  15. 2021Directional FDR Control for Sub-Gaussian Sparse GLMscited by 2
  16. 2021PPFS: Predictive Permutation Feature Selectioncited by 2
  17. 2021Distributed Sparse Feature Selection in Communication-Restricted Networkscited by 2
  18. 2022A Conditional Randomization Test for Sparse Logistic Regression in High-Dimensioncited by 2
  19. 2022Learning to Increase the Power of Conditional Randomization Testscited by 2
  20. 2023Optimal Sparse Recovery with Decision Stumpscited by 2
  21. 2023Adaptive False Discovery Rate Control with Privacy Guaranteecited by 2
  22. 2023Bayes optimal learning in high-dimensional linear regression with network side informationcited by 2
  23. 2023Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classificationcited by 2
  24. 2023Copula for Instance-wise Feature Selection and Rankingcited by 2
  25. 2024DeepLINK-T: deep learning inference for time series data using knockoffs and LSTMcited by 2
  26. 2024Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptopcited by 2
  27. 2025Membership Inference Attacks with False Discovery Rate Controlcited by 2
  28. 2026Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offscited by 2
  29. 2021Spatially relaxed inference on high-dimensional linear modelscited by 1
  30. 2021Controlling the False Split Rate in Tree-Based Aggregationcited by 1
  31. 2021Differentially Private Variable Selection via the Knockoff Filtercited by 1
  32. 2022Flexible variable selection in the presence of missing datacited by 1
  33. 2022Error-based Knockoffs Inference for Controlled Feature Selectioncited by 1
  34. 2022Black-box Selective Inference via Bootstrappingcited by 1
  35. 2022Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discoverycited by 1
  36. 2022Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Datacited by 1
  37. 2022GRASP: A Goodness-of-Fit Test for Classification Learningcited by 1
  38. 2023Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labelscited by 1

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