Comparing Causal Inference Methods for Point Exposures with Missing Confounders: A Simulation Study
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混杂变量缺失时点暴露因果推断方法的模拟比较
- 研究问题
- 电子健康记录中混杂与混杂变量缺失同时存在时，联合处理两者的平均处理效应估计，与先插补再调整的方法如何比较？
- 方法与思路
- 作者以减重手术长期体重研究为背景设计模拟，将既有联合框架的两个估计量与插补后结果回归或逆概率加权进行比较。
- 材料陈述的贡献
- 摘要称传统组合方法在部分情境中表现良好，但没有估计量统一占优；文章据模拟比较提出部分混杂变量缺失时的实践建议。
- 阅读关注点
- 应核对缺失机制和因果识别条件、插补模型与分析模型的兼容性，以及模拟中的误设情景和偏差、效率比较指标。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出识别条件、模拟参数及建议的适用范围。
原文摘要
Causal inference methods based on electronic health record (EHR) databases must simultaneously handle confounding and missing data. Vast scholarship exists aimed at addressing these two issues separately, but surprisingly few papers attempt to address them simultaneously. In practice, when faced with simultaneous missing data and confounding, analysts may proceed by first imputing missing data and subsequently using outcome regression or inverse-probability weighting (IPW) to address confounding. However, little is known about the theoretical performance of such $\textit{ad hoc}$ methods. In a recent paper Levis $\textit{et al.}$ outline a robust framework for tackling these problems together under certain identifying conditions, and introduce a pair of estimators for the average treatment effect (ATE), one of which is non-parametric efficient. In this work we present a series of simulations, motivated by a published EHR based study of the long-term effects of bariatric surgery on weight outcomes, to investigate these new estimators and compare them to existing $\textit{ad hoc}$ methods. While the latter perform well in certain scenarios, no single estimator is uniformly best. We conclude with recommendations for good practice in the face of partially missing confounders.