论文标题
评估对不符的敏感性：估计和推理
Assessing Sensitivity to Unconfoundedness: Estimation and Inference
论文作者
论文摘要
本文提供了一组方法，用于量化使用无共同度假设估计的治疗效果的鲁棒性（也称为可观察或条件独立性的选择）。具体而言，我们在各种治疗效果参数上估计并推断界限，例如平均治疗效果（ATE）和治疗对治疗（ATT）的平均效果，在非参数假设的非参数弛豫下，由标量敏感性参数参数c索引。这些放松可以根据c的值有限地选择不可观察。对于足够大的C，这些界限等于无假设的边界。使用非标准的引导方法，我们展示了如何为这些结合函数构造置信带，这些函数在所有c的所有值上都是均匀的。我们通过对国家支持工作示范计划的影响的经验应用来说明这些方法。我们将这些方法实现在同伴Stata模块中，以便于实践中使用。
This paper provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption (also known as selection on observables or conditional independence). Specifically, we estimate and do inference on bounds on various treatment effect parameters, like the average treatment effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Using a non-standard bootstrap method, we show how to construct confidence bands for these bound functions which are uniform over all values of c. We illustrate these methods with an empirical application to effects of the National Supported Work Demonstration program. We implement these methods in a companion Stata module for easy use in practice.
