The application of causal discovery to diseases like Alzheimer's (AD) is limited by the static graph assumptions of most methods; such models cannot account for an evolving pathophysiology, modulated by a latent disease pseudotime. We propose to apply an existing latent variable model to real-world AD data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age, then learning how causal relationships evolve. Pseudotime outperformed age in predicting diagnosis (AUC 0.82 vs 0.59). Incorporating minimal, disease-agnostic background knowledge substantially improved graph accuracy and orientation. Our framework reveals dynamic interactions between novel (NfL, GFAP) and established AD markers, enabling practical causal discovery despite violated assumptions.

翻译：将因果发现应用于阿尔茨海默病（AD）等疾病时，受限于大多数方法的静态图假设；此类模型无法解释由潜在疾病伪时间调制的演化病理生理学。我们提出将一种现有的潜在变量模型应用于真实世界AD数据，推断出一个伪时间，该伪时间根据数据驱动的疾病轨迹对患者进行排序，独立于实际年龄，进而学习因果关系如何演化。在预测诊断方面，伪时间优于年龄（AUC 0.82 对 0.59）。结合最小化、疾病无关的背景知识显著提高了图的准确性和方向性。我们的框架揭示了新型（NfL、GFAP）与已确立的AD标志物之间的动态相互作用，即使在假设被违反的情况下，也能实现实用的因果发现。