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 compares different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance.

中文翻译：

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使用逆概率加权回归调整对局部平均治疗效果进行双重稳健估计

我们重新审视了在控制变量可用时估计局部平均治疗效果 (LATE) 和治疗后的局部平均治疗效果 (LATT) 的问题，以使工具变量 (IV) 适当地外生或提高精度。与以前的方法不同，我们的双重稳健 (DR) 估计程序使用由 IV 倾向得分的倒数加权的准似然方法 - 所谓的逆概率加权回归调整 (IPWRA) 估计量。通过为倾向得分和结果模型正确选择模型，确保拟合值在由响应变量确定的逻辑范围内，从而产生具有吸引人的小样本属性的 LATE 和 LATT 的 DR 估计量。推理在分析和使用非参数引导方面都相对简单。我们的 DR LATE 和 DR LATT 估计器在模拟中运行良好。我们还提出了 Hausman 检验的 DR 版本，该版本比较了在单侧不依从情况下对被治疗者 (ATT) 的平均治疗效果的不同估计值。