融合时空建模与惩罚似然，提升医学因果推断准确性
Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
- 用隐马尔可夫模型估空间健康状态，图卷积网络捕捉时间轨迹
- 在模拟中实现比传统方法更低的偏差和更高预测精度
- 适合研究癌症、痴呆等难以直接观测治疗效果的疾病
本研究提出一种整合框架，用于克服传统单一模型在预测性因果推断中的局限。通过结合隐马尔可夫模型（HMM）进行空间健康状态估计，以及多任务多图卷积网络（MTGCN）捕捉时间结果轨迹，该框架将时空信息不对称地处理：在结果回归中作为内生变量，在倾向得分模型中作为外生变量，从而扩展标准双重稳健估计，联合提升偏差校正与预测精度。以癌症、痴呆和帕金森病等临床领域为例，因治疗效应难以直接观测，通过模拟潜在疾病动态，在不同条件下评估模型性能。实验表明，该框架能有效应对生物医学数据中常见的时空复杂性，显著推进预测性因果推断。
原文摘要 · Abstract (English)
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.
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