论文标题
具有高维数据
Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data
论文作者
论文摘要
本文提出了一个双重鲁棒的两阶段半参数差异估计器，用于使用高维数据估计异质治疗效果。我们的新估计器对模拟错过的特异性非常强大，并且允许但不需要比观测值更多的回归器。第一阶段允许使用一组机器学习方法来估计倾向得分。在第二阶段，我们得出了在部分线性规范的结果方程中，参数参数和未知函数的收敛速率。我们还提供偏见校正程序，以允许对异质治疗效果的有效推断。我们通过广泛的模拟研究评估有限样本性能。此外，对我们方法的例证进行了有关公平最低工资法对失业率的影响的真实数据分析。用于实现该方法的R软件包可在GitHub上获得。
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.
