Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data
This work addresses the challenge of causal inference in econometrics and statistics for researchers dealing with high-dimensional datasets, offering a robust method with practical tools like an R package, though it is incremental in building on existing semiparametric and machine learning approaches.
The paper tackles the problem of estimating heterogeneous treatment effects with high-dimensional data by proposing a doubly robust semiparametric difference-in-differences estimator, which is robust to model misspecifications and allows for many regressors, as demonstrated through simulations and a real-world application on the Fair Minimum Wage Act's effect on unemployment rates.
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.