Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
结合时空建模与惩罚经验似然的预测型因果推断
This study introduces an integrated framework for predictive causal inference designed to overcome limitations inherent in conventional single model approaches. Specifically, we combine a Hidden Markov Model (HMM) for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories. The framework asymmetrically treats temporal and spatial information regarding them as endogenous variables in the…