论文标题
对广义晚期模型的有效且可靠的估计
Efficient and Robust Estimation of the Generalized LATE Model
论文作者
论文摘要
本文研究了广义局部平均治疗效果（GLATE）模型中因果参数的估计，该模型的概括包括多价值处理和仪器。我们得出两种类型的参数的有效影响函数（EIF）和半参数效率结合（SPEB）：局部平均结构函数（LASF）和处理过的局部平均结构函数（LASF-T）。 EIF产生的力矩条件满足了两种鲁棒性特性：双重鲁棒性和Neyman正交性。根据稳健的力矩条件，我们提出了LASF和LASF-T的双重/辩论机器学习（DML）估计器。 DML估计量是半参数有效的，适用于高维设置。我们还提出了无限制的推理方法，这些方法可抵抗弱识别问题。作为经验应用，我们通过将开发方法应用于俄勒冈州健康保险实验，研究不同健康保险来源的影响。
This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. The DML estimator is semiparametric efficient and suitable for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
