Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
针对交错处理采纳场景，提出合成控制预测的置信区间，给出精确的非渐近覆盖概率保证，并讨论了多种因果预测目标，附有实证应用和软件包。
Abstract 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. 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.