Title: Doubly robust semiparametric difference-in-differences estimators with high-dimensional data
Authors: Jing Tao - University of Washington (United States) [presenting]
Abstract: A doubly robust two-stage semiparametric difference-in-difference estimator is proposed for estimating heterogeneous treatment effects with high-dimensional data. The new estimator is robust to model miss-specifications and allows for, but does not require, many more regressors than observations. The first stage allows a general set of machine learning methods to be used to estimate the propensity score. In the second stage, we derive the rates of convergence for both the parametric parameter and the unknown function under a partially linear specification for the outcome equation. We also provide bias correction procedures to allow for valid inference for the heterogeneous treatment effects. We evaluate the finite sample performance with extensive simulation studies. Additionally, a real data analysis on the effect of the Fair Minimum Wage Act on the unemployment rate is performed as an illustration of our method.