Papers about Knockoffs

A powerful and versatile framework for controlled variable selection.

General methodology

Sampling exact knockoffs

“Metropolized Knockoff Sampling”,
Stephen Bates, Emmanuel Candès, Lucas Janson, and Wenshuo Wang (2019). Link to the paper.

Approximate sampling of model-X knockoffs

“Deep Knockoffs”,
Yaniv Romano, Matteo Sesia and Emmanuel Candès. J. Am. Stat. Assoc. (2019). Link to the paper.

Main reference for model-X knockoffs

“Panning for Gold: ”Model-X“ Knockoffs for High-dimensional Controlled Variable Selection”,
Emmanuel Candès, Yingying Fan, Lucas Janson, and Jinchi Lv. J. R. Stat. Soc. B. (2018). Link to the paper.

Fixed-X knockoffs for high-dimensional linear models

“A Knockoff Filter for High-Dimensional Selective Inference”,
Rina Foygel Barber and Emmanuel Candès. Ann. Statist. 47 (2019). Link to the paper.

Main reference for Fixed-X knockoffs

“Controlling the False Discovery Rate via Knockoffs”,
Rina Foygel Barber and Emmanuel Candès. Ann. Statist. 43 (2015). Link to the paper.

Application-oriented methodology

Knockoffs for genome-wide association studies

“Multi-resolution localization of causal variants across the genome”,
Matteo Sesia, Eugene Katsevich, Stephen Bates, Emmanuel Candès, Chiara Sabatti. Nature Comm. (2020). Link to the paper.

“Gene Hunting with Knockoffs for Hidden Markov Models”,
Matteo Sesia, Chiara Sabatti, Emmanuel Candès. Biometrika (2018). Link to the paper.

Multilayer controlled variable selection with knockoffs

“Multilayer Knockoff Filter: Controlled variable selection at multiple resolutions”,
Eugene Katsevich, Chiara Sabatti. Ann. Appl. Stat. (2019). Link to the paper.

Theory of knockoffs

Robust inference with Model-X knockoffs

“Robust Inference with Knockoffs”,
Rina Foygel Barber, Emmanuel Candès and Richard Samworth. Ann. Statist. (2020). Link to the paper.

Type-II error analysis

“A power and Prediction Analysis for Knockoffs with Lasso Statistics”,
Asaf Weinstein, Rina Foygel Barber and Emmanuel Candès. arXiv:1712.06465 (2017). Link to the paper.