Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example.

中文翻译：

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使用调查和损耗加权数据估计广义倾向得分

因果推理的先前工作表明，在二元处理的倾向得分估计阶段和结果模型阶段使用调查抽样权重可以对所分析的二元处理的效果产生更稳健的估计。然而，迄今为止，尚未探索将这项工作扩展到连续治疗和暴露，也没有考虑如何处理倾向评分模型中的磨损权重。尽管如此，当研究人员拥有观察数据时，广义倾向评分（GPS）分析可用于估计持续治疗对结果的影响，并且这些数据集通常具有需要在分析中考虑的调查或损耗权重。在这里，我们扩展了先前的工作，并通过分析结果表明，在 GPS 估计阶段和连续治疗的结果模型阶段使用调查抽样或损耗权重可以产生比不这样做的估计器更稳健的估计器。仿真研究结果表明，尽管在两个估计阶段使用权重足以进行鲁棒估计，但这不是必需的，并且在某些情况下，在各种使用权重估计的方法下，无偏估计是可能的。分析师不知道我们的模拟研究的条件是否成立，因此在两个估计阶段使用权重可能会为减少潜在偏差提供保证。我们在一个实证例子的背景下讨论我们的结果的含义。