论文标题
使用平衡重量来针对重叠不良时处理的治疗效果
Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor
论文作者
论文摘要
在流行病学中通常使用反比概率权重来估计观察性研究的因果效应。研究人员通常可以专注于平均治疗效果或对使用反比概率加权估计器处理的平均治疗效果。但是，当治疗组和对照组之间的重叠率很差时，这会产生极端的权重，从而导致估计和较大的差异。反概率权重的一种替代方法是重叠权重，其针对观察到的特征最重要的人群。尽管基于重叠权重的估计在这种情况下产生的偏见较少，但因果估计可能难以解释。逆概率权重的一种替代方法是平衡权重，该权重在估计过程中直接靶向不平衡。在这里，我们探讨了平衡权重是否允许分析师针对因重叠不良而产生反比概率权重的情况下对治疗的平均治疗效果。我们进行了三项模拟研究和一个经验应用。我们发现，在许多情况下，平衡权重使分析师仍然可以针对治疗的平均治疗效果，即使重叠很差。我们表明，尽管重叠权重仍然是估计因果效应的关键工具，但可以通过使用平衡权重而不是逆概率权重来定位更熟悉的估计。
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.
