论文标题
使用逆概率加权回归调整对局部平均治疗效果的双重稳定估计
Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment
论文作者
论文摘要
我们重新审查了当可用的控制变量时估计局部平均治疗效果（晚）和对治疗（LATT）的局部平均治疗效果的问题，以使仪器变量（IV）适当外源性或提高精度。与以前的方法不同，我们的双重鲁棒（DR）估计程序使用了由IV倾向分数的反向加权的准类方法 - 所谓的反可能性加权回归调整（IPWRA）估计量。通过正确选择倾向分数和结果模型的模型，可以确保拟合值在响应变量确定的逻辑范围内，从而产生了后期和LATT的DR估计器，并具有吸引人的小样本属性。分析和使用非参数引导程序在分析上都是相对简单的。我们的DR DR DAR和LATT估算器在模拟中效果很好。我们还提出了DR版本的Hausman测试，可通过比较单方面违规情况下对经过处理的（ATT）的平均治疗效果的不同估计值来评估不符的假设。与比较OLS和IV估计值的通常测试不同，此过程对于治疗效应异质性是可靠的。
We revisit the problem of estimating the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) when control variables are available, either to render the instrumental variable (IV) suitably exogenous or to improve precision. Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so-called inverse probability weighted regression adjustment (IPWRA) estimators. By properly choosing models for the propensity score and outcome models, fitted values are ensured to be in the logical range determined by the response variable, producing DR estimators of LATE and LATT with appealing small sample properties. Inference is relatively straightforward both analytically and using the nonparametric bootstrap. Our DR LATE and DR LATT estimators work well in simulations. We also propose a DR version of the Hausman test that can be used to assess the unconfoundedness assumption through a comparison of different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance. Unlike the usual test that compares OLS and IV estimates, this procedure is robust to treatment effect heterogeneity.
