发表机构
Faculty of Economics, Keio University; RIKEN Center for Advanced Intelligence Project (AIP)（庆应义塾大学经济学部; 理化学研究所先进智能项目中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多组合成差分中异质性的有限总体推断，通过结合多种方法构建理论，经美国社区调查和县级《清洁空气法》应用验证，可揭示效应变化、验证分解，还发现捐赠者共享对标准误差有影响。
AI 中文摘要
合成差分广泛用于估计多个处理组相对于共同捐赠池的处理效果。当跨组重复使用相同捐赠者时，组特定估计在横截面存在依赖性，插件二阶矩会夸大效应异质性。本文开发了多组合成差分中异质性的有限总体推断方法，包括实现的组效应在观测组协变量上的投影等。理论结合了模块化第一阶段表示等。通过美国社区调查应用于《平价医疗法案》医疗补助扩展及县级《清洁空气法》应用验证了该方法，捐赠者共享会增加平均效应的标准误差，在某些应用中能揭示有意义的结果。
英文摘要
Empirical studies often estimate several synthetic-control or synthetic Difference-in-Differences effects from a common donor pool and then summarize their heterogeneity. Because the same donors enter several comparisons, the first-stage errors are jointly distributed and affect different targets differently. We characterize three regimes for linear fixed-set summaries: nonconcentration, root-rate donor exposure, and donornegligibility. We also show that removing shared-donor uncertainty from a quadratic summary requires the shared-donor covariance to vanish in every direction entering that summary. We take a valid joint first-order experiment for the effect vector as the input to the second stage. It yields simultaneous intervals for a prespecified linear family, a fixedgrid projection band, a centered effect-vector confidence region, image sets for total and explained heterogeneity and their ratio, and a sampling-center homogeneity test. For the hard-simplex SDID estimator, an operator-normalized numerical directional bootstrap re-estimates the donor and time weights in every draw. We use an exact critical-cone derivative to check the numerical approximation. In the Medicaid expansion application, omitting off-diagonal covariance understates uncertainty for the equal-jurisdiction mean but overstates it for a centered projection on the baseline uninsured rate. Persistent counterfactual mismatch is reported separately through deterministic sensitivity regions.
