This paper provides an orthogonal extension of the semiparametric difference-in-differences estimator proposed in earlier literature. The proposed estimator enjoys the so-called Neyman orthogonality (Chernozhukov et al., 2018), and thus it allows researchers to flexibly use a rich set of machine learning methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set in which the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude.

中文翻译：

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差异模型的双重/去偏机器学习

本文提供了先前文献中提出的半参数差分中差估计量的正交扩展。拟议的估计器享有所谓的Neyman正交性（Chernozhukov等人，2018），因此它使研究人员可以在第一步估计中灵活使用一组丰富的机器学习方法。当研究人员面对潜在控制变量的数量大于样本数量且不应用常规非参数估计方法（例如核和筛估计器）的高维数据集时，此功能特别有用。我使用这个正交的差异差估算器来评估关税降低对腐败的影响。实证结果表明，降低关税在很大程度上减少了腐败。