SpatialCausal : a spatially-aware causal inference deep learning model for out-of-hospital cardiac arrest survival prediction

Recently, numerous machine learning methods have been effectively applied to uncover spatial relationships between health risk factors and health outcomes. However, traditional machine learning methods often fail to address confounding bias, which arises when a common factor simultaneously influences both the treatment and the outcome – a challenge frequently encountered in observational studies. Deep learning-based causal inference models seek to mitigate confounding bias by learning balanced representations of covariates between treated and control groups, thereby reducing the dependence of treatment assignment on covariates. This enables accurate estimation of causal effects on health outcomes. Moreover, distinct geospatial patterns of risk exposure and health outcomes are common in many chronic diseases. Therefore, developing a spatially-aware causal inference model is essential for guiding geospatial health interventions. Here, we propose SpatialCausal, a spatially-aware deep learning-based causal inference model that explicitly integrates spatial, non-spatial, and unmeasured confounders, enabling accurate estimation of spatially-aware causal effects. We demonstrate the effectiveness of our approach through an application to Out-of-Hospital Cardiac Arrest survival outcome prediction. Our method surpasses state-of-the-art approaches and exhibits robust adaptability to various geospatial disease scenarios, making it a valuable tool for spatially-aware causal effect estimation in health geography.