Seminar by Fang Han, University of Washington

Abstract 

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 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.  

Bio

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. (Biostatistics) from Johns Hopkins University in 2015. Previously, he received his B.S. (Mathematics) from Peking University and M.S. (Biostatistics) from University of Minnesota.