Casual Astham Machine Learning

This project develops a spatiotemporal causal machine learning framework to quantify the impact of fine particulate matter (PM2.5) on asthma outcomes at the county level. Using monthly hospitalization and emergency department data from Colorado (2011-2019), the model integrates spatial and temporal dynamics to capture how pollution exposure varies across regions and over time. Unlike traditional statistical approaches that focus on correlation, this framework applies causal inference to estimate counterfactual scenarios, such as how asthma hospitalizations would change under reduced PM2.5 levels. Results reveal significant spatial heterogeneity, with the greatest health benefits occurring in communities facing overlapping environmental and social stressors. By identifying where pollution reduction would have the largest impact, this work provides a scalable, data-driven tool to inform targeted and equitable air quality and public health interventions.