Semiparametric Triple Difference Estimators
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AI 根据所提供的论文材料归类：摘要明确研究三重差分的因果识别，并分别构造面板数据与重复截面估计量。
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半参数三重差分估计量
- 研究问题
- 如何借助辅助领域数据识别三重差分中的处理组平均处理效应，并在面板与重复截面下构造稳健且有效的估计量？
- 方法与思路
- 作者提出结果回归和加权识别方法，以交叉拟合结合灵活的辅助函数估计，并在重复截面情形放宽样本构成不变假设。
- 材料陈述的贡献
- 摘要称给出效率、双重稳健性、根号样本量一致性及渐近正态性的成立条件；产假福利应用报告育龄女性时薪下降约百分之二点六。
- 阅读关注点
- 应核对辅助领域对应的趋势限制、重复截面构成变化如何处理、双重稳健性的具体组合，以及工资应用的识别依据。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出完整识别和收敛条件，工资结果仅为作者报告。
原文摘要
The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-in-differences framework through leveraging data from an auxiliary domain. Despite being commonly applied in empirical research, the triple difference framework has received relatively limited attention in the statistics literature. Specifically, investigating the intricacies of identification and the design of robust and efficient estimators for this framework has remained largely unexplored. This work aims to address these gaps in the literature. From the identification standpoint, we present outcome regression and weighting methods to identify the average treatment effect on the treated in both panel data and repeated cross-section settings. For the latter, we relax the commonly made assumption of time-invariant composition of units. From the estimation perspective, we develop semiparametric estimators for the triple difference framework in both panel data and repeated cross-sections settings. These estimators are based on the cross-fitting technique, and flexible machine learning tools can be used to estimate the nuisance components. We characterize conditions under which our proposed estimators are efficient, doubly robust, root-n consistent and asymptotically normal. As an application of our proposed methodology, we examined the effect of mandated maternity benefits on the hourly wages of women of childbearing age and found that these mandates result in a 2.6% drop in hourly wages.