论文标题
对处理和对照的平均治疗效果的差异估计
Variance estimation for the average treatment effects on the treated and on the controls
论文作者
论文摘要
常见的因果估计值包括平均治疗效果（ATE），治疗（ATT）的平均治疗效果以及对对照组（ATC）的平均治疗效果。使用增强的逆概率加权方法，明智地利用参数模型来产生双重稳健的估计器，即至少正确指定参数模型时一致的估计器。当我们评估这些估计量及其方差时，即当我们估计治疗和结果回归模型以及所需的治疗效果时，不确定性的三个来源是相关的。 在本文中，我们提出了计算归一化，双重稳定ATT和ATC估计器的方差的方法，并研究其有限的样品特性。我们考虑渐近三明治方差估计，标准引导程序以及两种野生引导方法。对于渐近近似值，我们通过估计方程式结合了上述不确定性。此外，与标准的引导程序不同，提议的野生引导方法使用估计函数的扰动通过独立分布的随机变量来扰动。我们进行了一项广泛的模拟研究，其中我们改变了治疗效果的异质性以及分配给主动治疗组的参与者的比例。我们使用对右心结构的关键患者的观察性研究说明了这些方法。
Common causal estimands include the average treatment effect (ATE), the average treatment effect of the treated (ATT), and the average treatment effect on the controls (ATC). Using augmented inverse probability weighting methods, parametric models are judiciously leveraged to yield doubly robust estimators, i.e., estimators that are consistent when at least one the parametric models is correctly specified. Three sources of uncertainty are associated when we evaluate these estimators and their variances, i.e., when we estimate the treatment and outcome regression models as well as the desired treatment effect. In this paper, we propose methods to calculate the variance of the normalized, doubly robust ATT and ATC estimators and investigate their finite sample properties. We consider both the asymptotic sandwich variance estimation, the standard bootstrap as well as two wild bootstrap methods. For the asymptotic approximations, we incorporate the aforementioned uncertainties via estimating equations. Moreover, unlike the standard bootstrap procedures, the proposed wild bootstrap methods use perturbations of the influence functions of the estimators through independently distributed random variables. We conduct an extensive simulation study where we vary the heterogeneity of the treatment effect as well as the proportion of participants assigned to the active treatment group. We illustrate the methods using an observational study of critical ill patients on the use of right heart catherization.
