Topic: | On imputation-based ATE estimators |
Date: | 01/03/2024 |
Time: | 10:30 am - 11:30 am |
Venue: | ERB LT · William M.W. Mong Engineering Building - LT · CUHK |
Category: | Latest Seminars and Events |
Speaker: | Professor Fang Han |
PDF: | Prof.-Fang-Han_1-MARCH-2024.pdf |
Details: | 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 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. |