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 outcome regression, and exogenous variables in the propensity score model, thereby expanding the standard doubly robust treatment effect estimation to jointly enhance bias correction and predictive accuracy. To demonstrate its utility, we focus on clinical domains such as cancer, dementia, and Parkinson disease, where treatment effects are challenging to observe directly. Simulation studies are conducted to emulate latent disease dynamics and evaluate the model performance under varying conditions. Overall, the proposed framework advances predictive causal inference by structurally adapting to spatiotemporal complexities common in biomedical data.

翻译：本研究提出了一种集成框架用于预测性因果推断，旨在克服传统单一模型方法的固有局限性。具体而言，我们将用于空间健康状态估计的隐马尔可夫模型与用于捕捉时间结果轨迹的多任务多图卷积网络相结合。该框架将时空信息非对称地处理为结果回归中的内生变量和倾向得分模型中的外生变量，从而将标准双重稳健处理效应估计扩展至联合增强偏差校正与预测准确性。为验证其效用，我们聚焦于癌症、痴呆症和帕金森病等临床领域，这些领域的治疗效果难以直接观测。通过模拟研究模拟潜在疾病动态，并在不同条件下评估模型性能。总体而言，所提框架通过结构性地适应生物医学数据中常见的时空复杂性，推动了预测性因果推断的发展。