Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants

TL;DR

This paper develops a double/debiased machine learning approach combined with regression calibration to accurately estimate the causal effects of specific air pollution constituents on health outcomes, accounting for measurement error and correlations.

Contribution

It introduces a novel method integrating DML with measurement error correction for causal inference in multi-pollutant studies, validated through simulations and real data application.

Findings

Identified two PM2.5 constituents with negative causal effects on cognitive function.

Demonstrated the estimator reduces bias and achieves nominal coverage in simulations.

Applied method to real data, revealing specific pollutants' health impacts.

Abstract

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.