论文标题
非随机分配的双重加权M估计和缺失结果
Doubly weighted M-estimation for nonrandom assignment and missing outcomes
论文作者
论文摘要
本文提出了一类新的M估计剂，该类别的重量是非随机治疗分配的双胞胎问题和缺失结果，这两者都是治疗效果文献中常见的问题。所提出的类以“鲁棒性”属性为特征，这使得在有条件的感兴趣模型（例如，均值或分位数函数）或两个加权函数中对参数错误指定具有弹性。作为领先的应用，本文讨论了两个特定因果参数的估计。在框架的参数组件的错误指定下，平均和分位数处理效应（ATE，QTE）可以表示为双重加权估计器的功能。关于ATE，本文表明，即使在缺失结果的情况下，提出的估计量也具有双重稳定性。最后，为了证明估算员在经验环境中的生存能力，它适用于Calonico和Smith（2017）从国家支持的工作培训计划中的重建样本。
This paper proposes a new class of M-estimators that double weight for the twin problems of nonrandom treatment assignment and missing outcomes, both of which are common issues in the treatment effects literature. The proposed class is characterized by a `robustness' property, which makes it resilient to parametric misspecification in either a conditional model of interest (for example, mean or quantile function) or the two weighting functions. As leading applications, the paper discusses estimation of two specific causal parameters; average and quantile treatment effects (ATE, QTEs), which can be expressed as functions of the doubly weighted estimator, under misspecification of the framework's parametric components. With respect to the ATE, this paper shows that the proposed estimator is doubly robust even in the presence of missing outcomes. Finally, to demonstrate the estimator's viability in empirical settings, it is applied to Calonico and Smith (2017)'s reconstructed sample from the National Supported Work training program.
