Identifying Treatment Effects Under Data Combination
当结果变量和协变量分别在不同数据集中观测时，研究了反事实分布和处理效应的识别问题，并利用单调重排不等式推导出参数的紧界。
We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate data sets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone rearrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.