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 ones. We introduce GST-UNet (G-computation Spatio-Temporal UNet), a theoretically grounded neural framework that combines a U-Net-based spatiotemporal encoder with regression-based iterative G-computation to estimate location-specific potential outcomes under complex intervention sequences. GST-UNet explicitly adjusts for time-varying confounders and captures non-linear spatial and temporal dependencies, enabling valid causal inference from a single observed trajectory in data-scarce settings. We validate its effectiveness in synthetic experiments and in a real-world analysis of wildfire smoke exposure and respiratory hospitalizations during the 2018 California Camp Fire. Together, these results position GST-UNet as a principled and ready-to-use framework for spatiotemporal causal inference, advancing reliable estimation in policy-relevant and scientific domains.

翻译：从时空观测数据中估计因果效应在公共卫生、环境科学和政策评估中至关重要，因为在这些领域中随机实验往往不可行。然而，现有方法要么依赖于强结构假设，要么无法处理关键挑战，如干扰、空间混杂、时间滞后效应以及时变混杂——即协变量受过去干预影响，进而影响未来干预。我们提出了GST-UNet（基于G-计算的时空U-Net），这是一个理论基础的神经框架，它将基于U-Net的时空编码器与基于回归的迭代G-计算相结合，用于估计复杂干预序列下特定位置的潜在结果。GST-UNet明确调整时变混杂因子，并捕捉非线性的空间和时间依赖关系，从而在数据稀缺的设定下，能够从单一观测轨迹进行有效的因果推断。我们在合成实验和2018年加州坎普山火期间野火烟雾暴露与呼吸道住院率的真实世界分析中验证了其有效性。这些结果共同表明，GST-UNet是一个原则性且即用的时空因果推断框架，推动了政策相关和科学领域中可靠估计的进展。