GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
GST-UNet：处理时变混杂的时空因果推断神经框架
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often infeasible. Existing approaches, however, either rely on strong structural assumptions or fail to handle key challenges such as interference, spatial confounding, temporal carryover, and time-varying confounding -- where covariates are influenced by past treatments and, in turn, affect future…