Finitely Heterogeneous Treatment Effect in Event-study
放松了双重差分中的平行趋势假设，通过引入潜在类型变量和类型特定的平行趋势，提出一种基于极值分类的类型特定DiD估计量，用于估计类型特定的平均处理效应，从而研究处理效应的异质性。
Abstract A key assumption of the differences-in-differences designs is that the average evolution of untreated potential outcomes is the same across different treatment cohorts: parallel trend assumption. In this paper, we relax the parallel trend assumption by assuming a latent type variable and developing a type-specific parallel trend. With a finite support assumption on the latent type and long pretreatment time periods, an extremum classifier consistently estimates the type assignment. Based on the classification, we propose a type-specific DiD estimator for type-specific ATT. By estimating the type-specific ATT, we study heterogeneity in treatment effect, in addition to heterogeneity in baseline outcomes.