Flexible and Efficient Estimation of Causal Effects with Error-Prone Exposures: A Control Variates Approach for Measurement Error
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含暴露测量误差的灵活高效因果效应估计：控制变量法
- 研究问题
- 当完整样本的暴露测量存在误差、仅少量验证样本拥有金标准测量时，如何灵活估计因果目标量并提高统计效率？
- 方法与思路
- 作者以控制变量法为基础，用全样本形成的方差缩减项增广验证样本中的初始一致估计量，同时利用含误差与无误差暴露测量。
- 材料陈述的贡献
- 摘要称框架适用于多种两阶段抽样设计，在标准因果假设下继承双重稳健性，并通过模拟及 HIV 电子健康记录应用展示表现。
- 阅读关注点
- 核对验证样本抽样机制、初始估计量一致性的要求，以及方差缩减项的构造和双重稳健性究竟对应哪些模型组合。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列明因果与抽样条件、效率界或比较数值，不能认为测量误差可无条件校正。
原文摘要
Exposure measurement error is a ubiquitous but often overlooked challenge in causal inference with observational data. Existing methods accounting for exposure measurement error largely rely on restrictive parametric assumptions, while emerging data-adaptive estimation approaches allow for less restrictive assumptions but at the cost of flexibility, as they are typically tailored towards rigidly-defined statistical quantities. There remains a critical need for assumption-lean estimation methods that are both flexible and possess desirable theoretical properties across a variety of study designs. In this paper, we introduce a general framework for estimation of causal quantities in the presence of exposure measurement error, adapted from the control variates approach of Yang and Ding (2019). Our method can be implemented in various two-phase sampling study designs, where one obtains gold-standard exposure measurements for a small subset of the full study sample, called the validation data. The control variates framework leverages both the error-prone and error-free exposure measurements by augmenting an initial consistent estimator from the validation data with a variance reduction term formed from the full data. We show that our method inherits double-robustness properties under standard causal assumptions. Simulation studies show that our approach performs favorably compared to leading methods under various two-phase sampling schemes. We illustrate our method with observational electronic health record data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.