This paper introduce the GST-UNet (G-computation Spatio-Temporal UNet), a novel neural framework designed for causal inference using spatiotemporal observational data, particularly when analyzing a single observed trajectory. This framework integrates a U-Net encoder with ConvLSTM and attention mechanisms to learn spatiotemporal dependencies and explicitly address challenges like interference, spatial confounding, and time-varying confounding. The core contribution is coupling this architecture with iterative G-computation to provide theoretically grounded identification and consistency guarantees for estimating location-specific potential outcomes. Empirical results, including synthetic experiments and a real-world analysis of wildfire smoke exposure and respiratory hospitalizations during the 2018 California Camp Fire, validate the method's ability to produce stable and accurate counterfactual estimates compared to existing baselines.