  This paper proposes a doubly robust two-stage semiparametric
difference-in-difference estimator for estimating heterogeneous treatment
effects with high-dimensional data. Our 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 Fair Minimum Wage Act on the unemployment rate is
performed as an illustration of our method. An R package for implementing the
proposed method is available on Github.
