报告题目：On imputation-based ATE estimators

报 告 人： Han Fang

时   间：2024年6月23日上午10:00-12:00

腾讯会议：357-494-355

报告人简介：

Han Fang is an associate professor in statistics, in economics (adjunct) at the University of Washington, and an affiliated investigator in Fred Hutchinson Cancer Research Center. He obtained his Ph.D. from the Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health in 2015. His research interest includes Rank- and graph-based methods, statistical optimal transport, mixture models, nonparametric and semiparametric regressions, time series analysis, and random matrix theory.

摘要：

Consider estimating the average treatment effect (ATE) by imputing the missing potential outcomes. In this talk I will show that (a) such imputations are all intrinsically estimating the covariate density ratio between treated and control, or equivalently, the propensity score; (b) combining imputation with a type of bias correction due to Rubin (1973) and Abadie and Imbens (2011) yields doubly robust and semiparametrically efficient ATE estimators; and (c) a double machine learning version exists; it produces similar theoretical guarantees under arguably milder conditions.

赌博平台

2024年6月12日

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