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20172021
most citedGene Hunting with Knockoffs for Hidden Markov Models

148 citations · 405 across the 8 of their papers we have counts for

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6 papers · 1 filter

stat.ME2021★ 7 cited

Searching for consistent associations with a multi-environment knockoff filter

Shuangning Li, Matteo Sesia, Yaniv Romano +2

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The…

stat.ME2020★ 55 cited

Causal Inference in Genetic Trio Studies

Stephen Bates, Matteo Sesia, Chiara Sabatti +1

We introduce a method to rigorously draw causal inferences---inferences immune to all possible confounding---from genetic data that include parents and offspring. Causal conclusion…

stat.ME2019★ 57 cited

A comparison of some conformal quantile regression methods

Matteo Sesia, Emmanuel J. Candès

We compare two recently proposed methods that combine ideas from conformal inference and quantile regression to produce locally adaptive and marginally valid prediction intervals u…

stat.ME2019★ 18 cited

Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"

Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès

In this paper we deepen and enlarge the reflection on the possible advantages of a knockoff approach to genome wide association studies (Sesia et al., 2018), starting from the disc…

stat.ME2018★ 113 cited

Deep Knockoffs

Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iterat…

stat.ME2017★ 148 cited

Gene Hunting with Knockoffs for Hidden Markov Models

Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès

Modern scientific studies often require the identification of a subset of relevant explanatory variables, in the attempt to understand an interesting phenomenon. Several statistica…