🤖 AI Summary
This study addresses exposure misclassification in causal inference of air pollution health effects due to individual mobility. Moving beyond the conventional measurement error paradigm, we propose a novel framework that formalizes population inter-regional movement as a geographic spillover interference process. We define and identify policy-relevant multivariate continuous-exposure causal quantities and rigorously prove that ignoring mobility induces systematic bias. Methodologically, we integrate mobile phone signaling, remote sensing, and ground-level monitoring data; estimate interference-adjusted causal effects via a Bayesian nonparametric model; and design a geographically weighted exposure reweighting strategy. Empirical analysis on the U.S. Medicare elderly population demonstrates that accounting for mobility substantially revises causal effect estimates of PM₂.₅ and NO₂ on mortality, improving exposure assessment accuracy and enhancing the interpretability and policy relevance of environmental health analyses.
📝 Abstract
We develop new methodology to improve our understanding of the causal effects of multivariate air pollution exposures on public health. Typically, exposure to air pollution for an individual is measured at their home geographic region, though people travel to different regions with potentially different levels of air pollution. To account for this, we incorporate estimates of the mobility of individuals from cell phone mobility data to get an improved estimate of their exposure to air pollution. We treat this as an interference problem, where individuals in one geographic region can be affected by exposures in other regions due to mobility into those areas. We propose policy-relevant estimands and derive expressions showing the extent of bias one would obtain by ignoring this mobility. We additionally highlight the benefits of the proposed interference framework relative to a measurement error framework for accounting for mobility. We develop novel estimation strategies to estimate causal effects that account for this spatial spillover utilizing flexible Bayesian methodology. Lastly, we use the proposed methodology to study the health effects of ambient air pollution on mortality among Medicare enrollees in the United States.
