Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods
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随机缺失协变量下倾向得分分析的多重插补：插补内与跨插补方法
- 研究问题
- 研究协变量随机缺失时，多重插补与倾向得分分析如何结合，重点比较插补内估计和跨插补平均的一致性。
- 方法与思路
- 作者分析先在各插补数据中估计效应再合并，以及先平均倾向得分或逆概率权重再估计效应的不同流程。
- 材料陈述的贡献
- 摘要称插补内方法可配合完整数据下一致的因果估计量；既有跨插补方法不一致，而平均逆概率权重的方法可一致。
- 阅读关注点
- 应核对插补模型与随机缺失条件、完整数据估计量的一致性要求，以及平均权重方法的适用范围和方差合并。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未完整列出插补条件，不能将一致性结论推广至任意缺失机制。
原文摘要
In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the within approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the across approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers.