Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor
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重叠不足时用平衡权重估计已处理者的处理效应
- 研究问题
- 当处理组与对照组协变量重叠不足、逆概率权重不稳定时，能否通过平衡权重继续估计已处理者的平均处理效应？
- 方法与思路
- 作者考察直接控制协变量不平衡的权重方法，通过三组模拟与一项实证应用，比较其在重叠不足时估计 ATT 的表现。
- 材料陈述的贡献
- 摘要称平衡权重在许多情形下仍可针对 ATT，而重叠权重面向另一个目标总体，为保留熟悉的效应目标提供选择。
- 阅读关注点
- 应核对平衡约束与权重构造、重叠不足的模拟方式、估计目标是否保持一致，以及偏差与方差的实际权衡。
- 阅读边界
- 这是 arXiv 预印本，不能确认同行评审或接收。摘要未说明完整识别条件，不能把有限情形的结果理解为消除了重叠要求。
原文摘要
Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers can typically focus on either the average treatment effect or the average treatment effect on the treated with inverse probability weighting estimators. However, when overlap between the treated and control groups is poor, this can produce extreme weights that can result in biased estimates and large variances. One alternative to inverse probability weights are overlap weights, which target the population with the most overlap on observed characteristics. While estimates based on overlap weights produce less bias in such contexts, the causal estimand can be difficult to interpret. One alternative to inverse probability weights are balancing weights, which directly target imbalances during the estimation process. Here, we explore whether balancing weights allow analysts to target the average treatment effect on the treated in cases where inverse probability weights are biased due to poor overlap. We conduct three simulation studies and an empirical application. We find that in many cases, balancing weights allow the analyst to still target the average treatment effect on the treated even when overlap is poor. We show that while overlap weights remain a key tool for estimating causal effects, more familiar estimands can be targeted by using balancing weights instead of inverse probability weights.