GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David Park, Xihaier Luo, Shinjae Yoo, Nathan Kallus
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often infeasible. However, existing approaches typically rely on strong structural assumptions or fail to account for 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 treatments and outcomes. In this work, we propose the **GST-UNet** (**G**-computation **S**patio-**T**emporal **UNet**), an end-to-end neural framework that integrates a U-Net-based spatiotemporal embedding with regression-based iterative G-computation to estimate location-specific potential outcomes under complex intervention sequences. The GST-UNet explicitly adjusts for time-varying confounders and accommodates non-linear spatial and temporal dependencies, enabling valid causal inference in settings with limited data and complex feedback dynamics. We demonstrate its effectiveness in synthetic experiments and apply it to estimate the impact of wildfire smoke on respiratory hospitalizations during the 2018 California Camp Fire. Our results highlight GST-UNet as a ready-to-use framework for practitioners seeking reliable causal estimates from spatiotemporal data, with the potential to advance causal inference across a wide range of policy-driven and scientific applications.