Transporting treatment effects from difference-in-differences studies
将双重差分研究的处理效应迁移至目标总体AI 解读 ↓
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将双重差分研究的处理效应迁移至目标总体
- 研究问题
- 当双重差分研究样本与政策关心的目标总体不同时，如何识别并估计目标总体效应，未观测混杂会如何限制迁移？
- 方法与思路
- 作者用因果图展示效应迁移假设，构造参数化公式计算、逆优势加权及基于有效影响函数的双重稳健估计量。
- 材料陈述的贡献
- 摘要称提出双重差分效应迁移的识别与估计框架，并强调未测混杂不能在所选效应尺度上修饰效应这一关键限制。
- 阅读关注点
- 应核对双重差分与迁移分别需要的假设、效应尺度的作用，以及双重稳健性质和纽约研究向全美外推的具体依据。
- 阅读边界
- 这是预印本，同行评审及期刊接收未获核实；摘要未完整列出识别条件，也未报告模拟数值或公共住房空气质量效应。
原文摘要
Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between the treatment and potential outcomes, such as in a randomized trial. We present a novel approach to identifying and estimating effects in a target population, based on DID conducted in a study sample that differs from the target population. We present a range of assumptions under which one may identify causal effects in the target population and employ causal diagrams to illustrate these assumptions. In most realistic settings, results depend critically on the assumption that any unmeasured confounders are not effect measure modifiers on the scale of the effect of interest (e.g., risk difference, odds ratio). We develop several estimators of transported effects, including g-computation, inverse odds weighting, and a doubly robust estimator based on the efficient influence function. Simulation results support theoretical properties of the proposed estimators. As an example, we apply our approach to study the effects of a 2018 US federal smoke-free public housing law on air quality in public housing across the US, using data from a DID study conducted in New York City alone.