Improving Causal Inference with Measurement Errors in Exposures and Confounders: A New Method and Its Application to Air Pollution Exposure Assessment and Epidemiology
暴露与混杂变量测量误差下的因果推断：方法及空气污染应用AI 解读 ↓
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暴露与混杂变量测量误差下的因果推断：方法及空气污染应用
- 研究问题
- 当多种暴露和混杂变量同时存在测量误差时，如何估计平均暴露效应，并避免污染混合物和暴露时间窗口分析产生偏差？
- 方法与思路
- 作者提出两阶段框架，先进行多维回归校准，再将校准变量用于 g-computation，并结合分布滞后变量分析污染与死亡风险。
- 材料陈述的贡献
- 摘要称讨论了线性及非线性潜在结果模型下的识别条件，模拟支持偏差修正，并报告 Cook County 污染与死亡风险的关联。
- 阅读关注点
- 应核对校准模型所需的验证信息、测量误差与混杂的条件，以及校准不确定性如何进入后续效应估计和置信区间。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列明识别条件，所报污染与死亡关联不能仅凭摘要确认为因果效应。
原文摘要
When exposure measurement error (EME), confounder measurement error (CME), or both are present, health effect estimates regarding exposure mixtures and critical exposure time-window may not represent the true effects. For example, in air pollution epidemiology, modeled estimates for multiple air pollutants and meteorological factors may serve as surrogates for exposures and confounders. Methods for simultaneously addressing EME and CME remain understudied. We developed a two-stage causal effect modeling framework to estimate average exposure/treatment effects (AEE) by addressing EME and CME. We identified conditions under which AEE is identifiable with minimal bias given linear or non-linear potential outcomes models and developed a new method, referred to as multi-dimensional regression calibration (MRC). The first stage of the framework estimates MRC models. The second stage estimates AEE by using g-computation with MR-Calibrated variables. Simulation analyses confirmed the bias-correction capability. As an application, we analyzed the association between air pollution and COVID-19 mortality in Cook County, Illinois. We developed machine learning-based 500m-gridded daily estimates of air pollutants and meteorological factors in a way for what we refer to as doubly EME&CME-correction. Using distributed lag variables, a one interquartile range (22.7ppb) increase in 3-week O3 exposure below 70ppb was associated with an 135.3% (95% CI: 68.4, 233.0) increase in COVID-19 mortality risk, comparable to that for PM2.5 exposure, which contradicts the previously reported no association for 3-week O3 in Cook County. At low levels, reducing pollution may have helped prevent premature deaths from COVID-19. Our new framework can address measurement error in multiple covariates simultaneously.