38 citations · 52 across the 12 of their papers we have counts for
13 papers
On regression with estimated covariates and conditional effects given the propensity score
Jiaqi Wu, Matteo Bonvini, Edward H. Kennedy +2
Motivated by the study of heterogeneous returns to education in Brand & Xie 2010, which considers how the effect of completing college on earnings varies with the (unknown) probabi…
Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length
Mengchu Zheng, Matteo Bonvini, Zijian Guo
We study inference on a low-dimensional functional in the presence of infinite-dimensional nuisance parameters. Classical inferential methods are typically based on Wald interv…
Doubly-robust inference and optimality in structure-agnostic models with smoothness
Matteo Bonvini, Edward H. Kennedy, Oliver Dukes +1
We study the problem of constructing an estimator of the average treatment effect (ATE) with observational data. The celebrated doubly-robust, augmented-IPW (AIPW) estimator genera…
Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients
Matteo Bonvini, Zhenghao Zeng, Miaoqing Yu +2
Laparoscopic surgery has been shown through a number of randomized trials to be an effective form of treatment for cholecystitis. Given this evidence, one natural question for clin…
Minimax optimal subgroup identification
Matteo Bonvini, Edward H. Kennedy, Luke J. Keele
Quantifying treatment effect heterogeneity is a crucial task in many areas of causal inference, e.g. optimal treatment allocation and estimation of subgroup effects. We study the p…
Covariate-assisted bounds on causal effects with instrumental variables
Alexander W. Levis, Matteo Bonvini, Zhenghao Zeng +2
When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the…
Sensitivity Analysis for Marginal Structural Models
Matteo Bonvini, Edward Kennedy, Valerie Ventura +1
We introduce several methods for assessing sensitivity to unmeasured confounding in marginal structural models; importantly we allow treatments to be discrete or continuous, static…
Fast convergence rates for dose-response estimation
Matteo Bonvini, Edward H. Kennedy
We consider the problem of estimating a dose-response curve. Continuous treatments arise often in practice, e.g. in the form of time spent on an operation, distance traveled to a l…
Incremental causal effects: an introduction and review
Matteo Bonvini, Alec McClean, Zach Branson +1
In this chapter, we review the class of causal effects based on incremental propensity scores interventions proposed by Kennedy [2019]. The aim of incremental propensity score inte…
Semiparametric sensitivity analysis: unmeasured confounding in observational studies
Razieh Nabi, Matteo Bonvini, Edward H. Kennedy +3
Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn fro…
Comment on "Statistical Modeling: The Two Cultures" by Leo Breiman
Matteo Bonvini, Alan Mishler, Edward H. Kennedy
Motivated by Breiman's rousing 2001 paper on the "two cultures" in statistics, we consider the role that different modeling approaches play in causal inference. We discuss the rela…
Causal Inference in the Time of Covid-19
Matteo Bonvini, Edward Kennedy, Valerie Ventura +1
In this paper we develop statistical methods for causal inference in epidemics. Our focus is in estimating the effect of social mobility on deaths in the Covid-19 pandemic. We prop…
Sensitivity analysis via the proportion of unmeasured confounding
Matteo Bonvini, Edward H Kennedy
In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant model…