From the Heinz College of Information Systems and Public Policy and Department of Statistics, Carnegie Mellon University, Pittsburgh, PA.
Department of Surgery, University of Pennsylvania, Philadelphia, PA.
Epidemiology. 2023 Sep 1;34(5):637-644. doi: 10.1097/EDE.0000000000001644. Epub 2023 Jun 21.
Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers often focus on either the average treatment effect or the average treatment effect on the treated with inverse probability weighting estimators. However, poor overlap in the baseline covariates between the treated and control groups can produce extreme weights that can result in biased treatment effect estimates. One alternative to inverse probability weights are overlap weights, which target the population with the most overlap on observed covariates. Although estimates based on overlap weights produce less bias in such contexts, the causal estimand can be difficult to interpret. An alternative to model-based inverse probability weights are balancing weights, which directly target imbalances during the estimation process, rather than model fit. Here, we explore whether balancing weights allow analysts to target the average treatment effect on the treated in cases where inverse probability weights lead to biased estimates due to poor overlap. We conduct three simulation studies and an empirical application. We find that balancing weights often allow the analyst to still target the average treatment effect on the treated even when overlap is poor. We show that although overlap weights remain a key tool, more familiar estimands can sometimes be targeted by using balancing weights.
逆概率权重在观察性研究中常用于估计因果效应。研究人员通常关注逆概率加权估计量的平均处理效应或处理组的平均处理效应。然而，在治疗组和对照组之间，基线协变量的重叠较差可能会产生极端权重，从而导致处理效应估计值存在偏差。逆概率权重的一种替代方法是重叠权重，它针对观察到的协变量最重叠的人群。尽管基于重叠权重的估计在这种情况下产生的偏差较小，但因果估计值可能难以解释。基于模型的逆概率权重的替代方法是平衡权重，它直接针对估计过程中的不平衡，而不是模型拟合。在这里，我们探讨了在由于重叠较差导致逆概率权重产生偏差的情况下，平衡权重是否允许分析师针对处理组的平均处理效应。我们进行了三项模拟研究和一项实证应用。我们发现，即使在重叠较差的情况下，平衡权重通常也允许分析师仍然针对处理组的平均处理效应。我们表明，尽管重叠权重仍然是一个关键工具，但有时使用平衡权重可以针对更熟悉的估计值。
