提出新非参数模型，可准确识别面板数据中的平均处理效应
Identification of Average Treatment Effects in Nonparametric Panel Models
- 构建非参数因子模型，通过一致估计器实现因果识别
- 证明了每个个体在各时期无处理时的期望结果可被一致估计
- 适用于性别工资差距等群体差异分解问题
本文研究面板数据中平均处理效应的识别问题。提出一种新颖的非参数因子模型，并证明了平均处理效应的可识别性。识别证明基于引入一个一致估计器。其核心结果是：对于每个个体和时间点，无处理情况下的期望结果存在一致估计；该结果可更广泛应用于群体结果差异的分解问题，例如备受关注的性别工资差距分析。
原文摘要 · Abstract (English)
This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for example in problems of decompositions of group-level differences in outcomes, such as the much-studied gender wage gap.
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