论文标题
采用交错治疗的合成控制中的不确定性定量
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
论文作者
论文摘要
我们提出了有原则的预测间隔，以量化具有交错的治疗方法的设置中大量合成控制预测（或估计量）的不确定性，并提供精确的非反应覆盖率概率保证。从方法论的角度来看，我们提供了有关要预测不同因果数量的详细讨论，我们称之为因果预测，允许在可能不同时间点采用治疗的多个处理单元。从理论的角度来看，我们的不确定性量化方法通过（i）涵盖交错的采用设置中的一大批因果预测，（ii）允许具有非线性约束的合成控制方法，（iii）提出可扩展的可伸缩式圆锥形的方法，并跨越可扩展的型号驱动式的启动式驱动器驱动器，并在（IV驱动的参数选择）中，（IV）访问（IV），（IV）的范围（IIC IIC II iv）时期。我们通过研究经验应用，研究经济自由化对撒哈拉以南非洲国家的实际GDP的影响来说明我们的方法论。 Python，R和Stata提供了配套软件包。
We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly different points in time. From a theoretical perspective, our uncertainty quantification methods improve on prior literature by (i) covering a large class of causal predictands in staggered adoption settings, (ii) allowing for synthetic control methods with possibly nonlinear constraints, (iii) proposing scalable robust conic optimization methods and principled data-driven tuning parameter selection, and (iv) offering valid uniform inference across post-treatment periods. We illustrate our methodology with an empirical application studying the effects of economic liberalization on real GDP per capita for Sub-Saharan African countries. Companion software packages are provided in Python, R, and Stata.
