纠正多个污染源测量误差，精准评估其对认知功能的因果影响
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
- 用回归校准+双重机器学习修正污染监测误差
- 实证发现溴和锰对认知功能有显著负向因果影响
- 适合做环境健康因果推断的研究者参考
在流行病学研究中，常以个体最近监测站数据代表暴露水平，但此方法存在测量误差，尤其在评估相关污染物构成成分的因果效应时易引入偏差。本文针对主研究中某一成分的因果效应估计问题，结合外部验证研究的广义估计方程拟合线性回归校准模型，扩展双重/去偏机器学习（DML）方法，以校正测量误差并估计目标效应。证明了该方法的相合性并推导了渐近方差。模拟结果表明，该估计器在多数设定下显著降低偏差并实现名义覆盖概率。应用该方法于护士健康研究，评估PM2.5组分对认知功能的影响，经误差校正后识别出两个具有负向因果效应的成分：Br和Mn。
原文摘要 · Abstract (English)
One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual's nearest monitor. This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in estimating the health effects of air pollution and its constituents, especially when evaluating the causal effects of correlated multi-pollutant constituents measured with correlated error. This paper addresses estimation and inference for the causal effect of one constituent in the presence of other PM2.5 constituents, accounting for measurement error and correlations. We used a linear regression calibration model, fitted with generalized estimating equations in an external validation study, and extended a double/debiased machine learning (DML) approach to correct for measurement error and estimate the effect of interest in the main study. We demonstrated that the DML estimator with regression calibration is consistent and derived its asymptotic variance. Simulations showed that the proposed estimator reduced bias and attained nominal coverage probability across most simulation settings. We applied this method to assess the causal effects of PM2.5 constituents on cognitive function in the Nurses' Health Study and identified two PM2.5 constituents, Br and Mn, that showed a negative causal effect on cognitive function after measurement error correction.
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