Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. We introduce the Causal Diffusion Model (CDM), the first denoising diffusion probabilistic approach explicitly designed to generate full probabilistic distributions of counterfactual outcomes under sequential interventions. CDM employs a novel residual denoising architecture with relational self-attention, capturing intricate temporal dependencies and multimodal outcome trajectories without requiring explicit adjustments (e.g., inverse-probability weighting or adversarial balancing) for confounding. In rigorous evaluation on a pharmacokinetic-pharmacodynamic tumor-growth simulator widely adopted in prior work, CDM consistently outperforms state-of-the-art longitudinal causal inference methods, achieving a 15-30% relative improvement in distributional accuracy (1-Wasserstein distance) while maintaining competitive or superior point-estimate accuracy (RMSE) under high-confounding regimes. By unifying uncertainty quantification and robust counterfactual prediction in complex, sequentially confounded settings, without tailored deconfounding, CDM offers a flexible, high-impact tool for decision support in medicine, policy evaluation, and other longitudinal domains.

翻译：在纵向数据中预测反事实结果（其中序贯治疗决策高度依赖于患者状态的动态演变）至关重要，但由于存在复杂的时间依赖性混杂因素以及现有方法对不确定性量化的不足，这一任务极具挑战性。我们提出了因果扩散模型（CDM），这是首个专为生成序贯干预下反事实结果完整概率分布而设计的去噪扩散概率方法。CDM采用了一种创新的残差去噪架构，结合关系型自注意力机制，能够捕捉复杂的时间依赖性和多模态结果轨迹，且无需对混杂因素进行显式调整（如逆概率加权或对抗性平衡）。在既往研究中广泛使用的药代动力学-药效学肿瘤生长模拟器上进行严格评估后，CDM始终优于最先进的纵向因果推断方法：在高混杂场景下，其分布精度（1-Wasserstein距离）实现了15-30%的相对提升，同时保持了具有竞争力或更优的点估计精度（RMSE）。通过统一复杂序贯混杂场景中的不确定性量化与稳健的反事实预测，且无需定制解混杂策略，CDM为医学决策支持、政策评估及其他纵向领域提供了灵活且高影响力的工具。