LaCSH: a spatial and causal framework for modeling socio-economic health
Conference
64th ISI World Statistics Congress
Format: IPS Abstract
Keywords: "bayesian, "social, "spatial, causal inference, hierarchical, propensity_score_adjustment
Session: IPS 194 - Frontiers in Data Science, Health, and the Environment
Thursday 20 July 2 p.m. - 3:40 p.m. (Canada/Eastern)
Abstract
We develop a model-based Latent Causal Socioeconomic Health (LaCSH) index, with uncertainty bounds, at the national level. Extending the latent health factor index (LHFI) modeling approach to assess ecosystem health, LaCSH integratively models the hierarchical relationship among the nation's societal health or well-being (latent / intangible), socio-economic metrics (e.g., GDP), the covariates that drive the notion of well-being (e.g., natural resources), and a continuous variable that reflects policy (e.g., government mandated maternity leave days). In addition to making statistical inference for socio-economic health, LaCSH facilitates the evaluation of any causal impact of the policy on health. A formal spatial component in the LaCSH framework allows us to compare the socio-economic health of countries around the world based on various metrics, covariates, and two different policy variables that pertain to socio-economic well-being. This is joint work led by FS Kuh with AH Westveld (https://arxiv.org/abs/2009.12217).