标题： 解开双重稳健估计：重叠的作用 显示英文标题

标题： Demystify Doubly-Robust Estimation: The Role of Overlap

摘要： 双重稳健（DR）估计器在观察性研究中评估因果效应时很受欢迎，通常被认为比逆概率加权（IPW）或结果建模单独使用更可取，因为它对模型误设提供了额外的保护。 然而，双重稳健性是一个渐近性质，在有限样本中可能不成立。 我们研究了DR估计器的有限样本性能如何依赖于比较组之间的协变量重叠程度。 通过在不同协变量重叠程度和模型规范的各种情景下进行分析说明和广泛的模拟，我们检查了DR估计器相对于IPW和结果建模估计器的偏差和方差。 我们发现：（i）结果模型的规范对DR估计的影响比倾向得分模型的规范更强，并且随着重叠程度的降低，这种主导作用会增强；（ii）在重叠较差的情况下，无论模型规范如何，DR估计器通常会放大极端权重（大偏差和/或方差）的不利影响，并且通常劣于IPW和结果建模估计器。 作为实践指南，我们建议在应用中始终首先检查重叠程度。 在重叠较差的情况下，分析人员应考虑通过修剪或重叠加权等方法将目标人群转移到具有足够重叠的子人群中。 显示英文摘要

摘要： The doubly-robust (DR) estimator is popular for evaluating causal effects in observational studies and is often perceived as more desirable than inverse probability weighting (IPW) or outcome modeling alone because it provides extra protection against model misspecification. However, double robustness is an asymptotic property that may not hold in finite samples. We investigate how the finite sample performance of the DR estimator depends on the degree of covariate overlap between comparison groups. Using analytical illustrations and extensive simulations under various scenarios with different degrees of covariate overlap and model specifications, we examine the bias and variance of the DR estimator relative to IPW and outcome modeling estimators. We find that: (i) specification of the outcome model has a stronger influence on the DR estimates than specification of the propensity score model, and this dominance increases as overlap decreases; (ii) with poor overlap, the DR estimator generally amplifies the adverse consequences of extreme weights (large bias and/or variance) regardless of model specifications, and is often inferior to both the IPW and outcome modeling estimators. As a practical guide, we recommend always first checking the degree of overlap in applications. In the case of poor overlap, analysts should consider shifting the target population to a subpopulation with adequate overlap via methods such as trimming or overlap weighting.