Triple Difference Designs with Heterogeneous Treatment Effects
异质性处理效应下的三重差分设计AI 解读 ↓
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异质性处理效应下的三重差分设计
- 研究问题
- 当人口子群的潜在处理效应不同，常规三重差分参数能否支持子群间因果比较？应如何定义并估计更适当的比较目标？
- 方法与思路
- 作者提出受处理者平均处理效应的受控差异参数，给出其识别假设和双重稳健估计量，并通过模拟及实证比较两类参数。
- 材料陈述的贡献
- 摘要称指出常规三重差分在子群效应异质时的因果解释问题，提出可用于子群因果比较的新目标及相应识别、估计方案。
- 阅读关注点
- 应核对受控差异的干预含义、子群构成调整与识别假设，以及双重稳健性依赖哪些模型和两种参数为何产生差异。
- 阅读边界
- 所给版本为预印本，不能据此确认同行评审或期刊接收；摘要未写明识别假设、稳健性条件及实证对象，不能判断实际适用性。
原文摘要
Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows another subgroup of the population -- potentially less impacted by the treatment -- to serve as a comparison for the subgroup of interest. While literature on difference-in-differences has discussed heterogeneity in treatment effects between treated and control groups or over time, relatively little attention has been given to triple difference designs and the implications of heterogeneity in treatment effects in this setting. In this paper, I show that the parameter identified under common triple difference assumptions does not allow for causal interpretation of differences between subgroups when subgroups may differ in their underlying (unobserved) treatment effects. I propose a new parameter of interest, the controlled difference in average treatment effects on the treated, which allows for causal comparisons between subgroups. I then propose identification assumptions and doubly-robust estimators for this parameter. I use a simulation study to highlight the desirable finite-sample properties of these estimators, as well as to show the difference between the two parameters. An empirical application shows the importance of considering treatment effect heterogeneity in practical applications.