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.
@article{arxiv.2511.04619,
title = {Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling},
author = {Natalia Glazman and Jyoti Mangal and Pedro Borges and Sebastien Ourselin and M. Jorge Cardoso},
journal= {arXiv preprint arXiv:2511.04619},
year = {2025}
}
Comments
Accepted to the NeurIPS 2025 Workshop on CauScien: Uncovering Causality in Science