论文标题
评估合成控制治疗治疗效果对错误指定误差的敏感性
Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error
论文作者
论文摘要
我们提出了对合成控制（SC）治疗效应估计的敏感性分析，以询问SC方法已得到充分指定的假设，即选择权重以最大程度地减少治疗前预测误差可以准确预测反事实后处理后的结果。我们的数据驱动过程恢复了与SC方法发生的错误指定误差的假设一致的一组治疗效果，最多是使用SC估计器预测某些控制单元的结果时，最多是可观察到的错误指定误差。我们表明，在错误指定错误的一个定义下，我们的程序提供了一种简单的几何动机，将估计的治疗效果与安慰剂残留物的分布进行比较以评估估计信誉。当我们将程序应用于报告SC估计的几项规范研究时，我们将广泛确认源文件得出的结论。
We propose a sensitivity analysis for Synthetic Control (SC) treatment effect estimates to interrogate the assumption that the SC method is well-specified, namely that choosing weights to minimize pre-treatment prediction error yields accurate predictions of counterfactual post-treatment outcomes. Our data-driven procedure recovers the set of treatment effects consistent with the assumption that the misspecification error incurred by the SC method is at most the observable misspecification error incurred when using the SC estimator to predict the outcomes of some control unit. We show that under one definition of misspecification error, our procedure provides a simple, geometric motivation for comparing the estimated treatment effect to the distribution of placebo residuals to assess estimate credibility. When we apply our procedure to several canonical studies that report SC estimates, we broadly confirm the conclusions drawn by the source papers.
