A Spatiotemporal Causal Machine Learning Framework for Quantifying the Impact of PM 2.5 on Asthma at the Community Level

Abstract Air pollution, particularly fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM 2.5 ), is a leading environmental risk factor for respiratory diseases. While previous studies have investigated the biological mechanisms linking PM 2.5 to asthma exacerbation, these studies are unable to scale for population‐level assessments. In contrast, most regional analyses rely on traditional statistical models that primarily quantify associations, limiting estimation of health changes under hypothetical pollution interventions. This study introduces a spatiotemporal causal machine learning framework to estimate PM 2.5 ‐related changes in asthma outcomes at the county level. Using county‐level monthly hospitalization and emergency department records from 2011 to 2019 in Colorado, the framework integrates spatial and temporal representation learning to model geographic interdependence and temporal dynamics in exposure–response relationships. Further, through a causal inference approach, the model estimates model‐based counterfactual health outcomes under hypothetical PM 2.5 interventions, moving beyond regression‐based correlation analysis. Results show that the model‐based counterfactual estimates exhibit spatial and population heterogeneity. Larger estimated effects were observed in several communities experiencing compounded environmental and social stressors, while uncertainty was greater in data‐sparse counties.