Socioeconomic Drivers of Physical Morbidity Across U.S. Counties: A Spatial Causal Inference Approach

TL;DR

This paper develops a spatial causal inference framework using spectral basis functions and generalized propensity scores to analyze how socioeconomic factors influence physical health across U.S. counties.

Contribution

It introduces a novel methodology combining spectral basis functions with doubly robust propensity scores for spatial causal analysis of continuous treatments.

Findings

Identified the effects of six socioeconomic predictors on unhealthy days in U.S. counties.

Addressed spatial autocorrelation and confounding in causal inference.

Framework applicable to public health policy analysis.

Abstract

Identifying the causal effects of socioeconomic determinants on population health is of many great interests - from statistical methodology development to public health practitioners and policy developments. The statistical side of the problem needs to address several questions: spatial autocorrelation in both exposures and outcomes, confounding between treatments and covariates, and the need for geographically logical inference. We address these jointly by using spectral basis functions - Moran Eigenvector Maps and ICAR precision matrix eigenvectors - within a doubly robust generalized propensity score estimator for continuous treatments. Applied to 2022 county health data across the U.S. counties, the framework identifies the effect of six chosen predictors on the average physically unhealthy days per month. Possible further applications and methodological extensions are also discussed as future directions from this research.