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 applied to several canonical studies that use the SC method, our procedure demonstrates that the signs of most of those results are relatively robust.

翻译：我们提议对合成控制(SC)处理效果估计进行敏感度分析,以质询以下假设:SC方法非常具体,即选择权重以尽量减少预处理预测错误,可以准确预测反实际情况处理后的结果。我们的数据驱动程序回收了一套治疗效果,这符合以下假设:SC方法发生的误差最多是使用SC测算器预测某个控制单位的结果时发生的可观察到的误差。我们表明,根据一个误差定义,我们的程序提供了一个简单、几何性动机,将估计的治疗效果与分配安慰剂残留来评估可信度的估计数进行比较。当应用到使用SC方法的数项理论性研究时,我们的程序表明,大多数结果的迹象相对可靠。