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.

翻译：双重稳健（DR）估计器在观察性研究中常用于评估因果效应，通常被认为比单独使用逆概率加权（IPW）或结果建模更可取，因为它能提供额外的模型误设保护。然而，双重稳健性是一种渐近性质，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间协变量重叠的程度。通过在不同协变量重叠程度和模型设定下的解析说明和广泛模拟，我们检验了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）结果模型的设定对DR估计的影响比倾向得分模型的设定更强，且这种主导作用随着重叠的减少而增加；（ii）在重叠较差的情况下，无论模型设定如何，DR估计器通常会放大极端权重（较大偏差和/或方差）的不利后果，并且往往劣于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，分析者应考虑通过修剪或重叠加权等方法，将目标群体转移到具有充分重叠的子群体。