Non-communicable disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models cannot provide causal interpretable predictions and estimate treatment effects, limiting their decision-making perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncover causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by the treatment of descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions. Source code is in \href{https://github.com/DanielSun94/CFPA}{here}.

翻译：非传染性疾病是导致死亡的主要原因，因此亟需准确预测疾病进展并做出知情的临床决策。机器学习模型通过捕捉患者特征中的非线性模式，在该领域展现出应用前景。然而，现有基于机器学习的模型无法提供因果可解释的预测，也无法评估治疗效果，这限制了其决策视角。在本研究中，我们提出一种名为因果轨迹预测（CTP）的新模型以解决该局限性。CTP模型结合了轨迹预测与因果发现，既能准确预测疾病进展轨迹，又能揭示特征间的因果关系。通过将因果图融入预测过程，CTP确保祖先特征不受后代特征治疗的影响，从而增强模型的可解释性。即使存在未测量的混杂因子，CTP仍能通过估计治疗效果边界，为临床决策提供重要见解。我们采用模拟数据集和真实医疗数据集评估CTP的性能。实验结果表明，我们的模型取得了令人满意的表现，凸显了其在辅助临床决策方面的潜力。源代码见\href{https://github.com/DanielSun94/CFPA}{此处}。