Flexible Estimation of Treatment Effect Parameters
针对具有单元结构的识别策略，提出灵活估计处理效应参数的方法，允许跨单元异质性，并探讨在参数或半参数约束下提高估计效率的途径。
A variety of identification strategies have a common cell structure, in which the observed heterogeneity of the regression defines a partition of the sample into cells. Typically in the presence of exogenous covariates that define the cell structure, identification assumptions are imposed conditional on each value of the covariate, or cell by cell. Treatment effects across cells are typically heterogeneous. Researchers might be interested in unconditional parameters which are the averaged treatment effects across the cells. Alternatively, treatment effects can be estimated more efficiently if researchers are willing to impose additional parametric and semiparametric structures on the heterogeneous treatment effects across cells.