标题： 改进的平均处理效应局部效率估计：双稳健性及其他 显示英文标题

标题： Improved Estimation of Average Treatment Effects on the Treated: Local Efficiency, Double Robustness, and Beyond

摘要： 估计接受治疗者的平均处理效应（ATT）是计量经济学和统计学中因果推断的重要课题。 这个问题似乎常常被视为估计总体平均处理效应（ATE）问题的一个简单修改或扩展。 然而，与ATE的估计不同，倾向得分对于ATT的估计不再是一个附属变量。 本文中，我们回顾了用于估计ATT的半参数理论以及使用有效影响函数来导出局部有效且双重稳健的增广逆概率加权（AIPW）估计器。 此外，我们讨论了通过发展校准回归和似然估计器来改进AIPW估计，这些估计器不仅局部有效且双重稳健，而且当倾向得分模型正确指定但结果回归模型可能被错误指定时，能够内在地实现比AIPW估计器更小的方差。 最后，我们呈现了两个模拟研究和一个计量经济学应用，以展示所提出方法相对于现有方法的优势。 显示英文摘要

摘要： Estimation of average treatment effects on the treated (ATT) is an important topic of causal inference in econometrics and statistics. This problem seems to be often treated as a simple modification or extension of that of estimating overall average treatment effects (ATE). However, the propensity score is no longer ancillary for estimation of ATT, in contrast with estimation of ATE. In this article, we review semiparametric theory for estimation of ATT and the use of efficient influence functionsto derive augmented inverse probability weighted (AIPW) estimators that are locally efficient and doubly robust. Moreover, we discuss improved estimation over AIPW by developing calibrated regression and likelihood estimators that are not only locally efficient and doubly robust, but also intrinsically efficient in achieving smaller variances than AIPW estimators when a propensity score model is correctly specified but an outcome regression model may be misspecified. Finally, we present two simulation studies and an econometric application to demonstrate the advantage of the proposed methods when compared with existing methods.