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.

翻译：识别社会经济决定因素对人口健康的因果效应，对于从统计方法开发到公共卫生实践和政策制定等领域都具有重要意义。该问题的统计层面需要解决几个关键问题：暴露因子和结局变量中的空间自相关性、处理变量与协变量之间的混淆偏倚，以及地理逻辑推断的必要性。我们通过使用谱基函数——莫兰特征向量图和ICAR精度矩阵特征向量——结合连续处理变量的双重稳健广义倾向性得分估计器，共同解决了上述问题。将该框架应用于2022年美国各县的县级健康数据，识别了六个选定预测因子对每月平均身体不健康天数的影响。本文还探讨了该研究未来的可能应用与方法论扩展方向。