arXiv subjects

Matteo Sesia

Publications and source records attributed to Matteo Sesia.

41 records · Page 3Linked to original sources

arXiv2020

Causal Inference in Genetic Trio Studies

We introduce a method to rigorously draw causal inferences---inferences immune to all possible confounding---from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a novel conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed Digital Twin Test compares an observed offspring to carefully constructed synthetic offspring from the same parents in order to determine statistical significance, and it can leverage any black-box multivariate model and additional non-trio genetic data in order to increase power. Crucially, our inferences are based only on a well-established mathematical description of the rearrangement of genetic material during meiosis and make no assumptions about the relationship between the genotypes and phenotypes.

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arXiv2019

A comparison of some conformal quantile regression methods

We compare two recently proposed methods that combine ideas from conformal inference and quantile regression to produce locally adaptive and marginally valid prediction intervals under sample exchangeability (Romano et al., 2019; Kivaranovic et al., 2019). First, we prove that these two approaches are asymptotically efficient in large samples, under some additional assumptions. Then we compare them empirically on simulated and real data. Our results demonstrate that the method in Romano et al. (2019) typically yields tighter prediction intervals in finite samples. Finally, we discuss how to tune these procedures by fixing the relative proportions of observations used for training and conformalization.

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arXiv2019

Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"

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 discussions in Bottolo & Richardson (2019); Jewell & Witten (2019); Rosenblatt et al. (2019) and Marchini (2019). The discussants bring up a number of important points, either related to the knockoffs methodology in general, or to its specific application to genetic studies. In the following we offer some clarifications, mention relevant recent developments and highlight some of the still open problems.

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arXiv2018

Deep Knockoffs

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 iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion is inspired by the popular maximum mean discrepancy in machine learning and can be thought of as measuring the distance to pairwise exchangeability between original and knockoff features. By building upon the existing model-X framework, we thus obtain a flexible and model-free statistical tool to perform controlled variable selection. Extensive numerical experiments and quantitative tests confirm the generality, effectiveness, and power of our deep knockoff machines. Finally, we apply this new method to a real study of mutations linked to changes in drug resistance in the human immunodeficiency virus.

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arXiv2017

Gene Hunting with Knockoffs for Hidden Markov Models

Modern scientific studies often require the identification of a subset of relevant explanatory variables, in the attempt to understand an interesting phenomenon. Several statistical methods have been developed to automate this task, but only recently has the framework of model-free knockoffs proposed a general solution that can perform variable selection under rigorous type-I error control, without relying on strong modeling assumptions. In this paper, we extend the methodology of model-free knockoffs to a rich family of problems where the distribution of the covariates can be described by a hidden Markov model (HMM). We develop an exact and efficient algorithm to sample knockoff copies of an HMM. We then argue that combined with the knockoffs selective framework, they provide a natural and powerful tool for performing principled inference in genome-wide association studies with guaranteed FDR control. Finally, we apply our methodology to several datasets aimed at studying the Crohn's disease and several continuous phenotypes, e.g. levels of cholesterol.

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