Demystify Doubly-Robust Estimation: The Role of Overlap
理解双重稳健估计：协变量重叠的作用AI 解读 ↓
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理解双重稳健估计：协变量重叠的作用
- 研究问题
- 比较组协变量重叠程度如何影响双重稳健估计的有限样本偏差与方差，其表现何时弱于逆概率加权或结果建模？
- 方法与思路
- 作者结合解析示例与多场景模拟，改变协变量重叠和模型设定，比较双重稳健、逆概率加权及结果模型估计。
- 材料陈述的贡献
- 摘要称重叠越差，结果模型设定对双重稳健估计的影响越强，极端权重风险也越突出，并建议检查重叠和调整目标人群。
- 阅读关注点
- 核对重叠程度的度量、模拟中的模型失配与样本规模，以及修剪或重叠加权改变目标总体后效应参数的解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未获核实；摘要中的性能比较依赖所考察场景，不能视为双重稳健估计普遍劣势。
原文摘要
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.