Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)
提出一种局部有效的半参数数据组合问题估计量，具有双稳健性，可用于估计处理组平均处理效应、两样本工具变量模型等，并在实证中用于分析黑人与白人工资差异。
We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, poverty maps, and semiparametric difference-in-differences. In an empirical application, we use our procedure to characterize residual Black--White wage inequality after flexibly controlling for “premarket” differences in measured cognitive achievement. Supplementary materials for this article are available online.