HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data

Abstract:Estimating potential outcomes under alternative treatment sequences is essential for personalized decision-making, but remains challenging with high-dimensional covariates and time-varying confounding. In this study, we propose HiSTOR (High-dimensional Structured Treatment-Outcome Representation), an end-to-end framework that integrates nonlinear dimension reduction with longitudinal counterfactual prediction. The central idea is to learn structured low-dimensional representations of both current patient states and evolving histories, according to their shared and distinct relationships with treatment assignment and outcomes. This extends latent-space covariate adjustment to longitudinal settings, jointly addressing covariate dimensionality and historical complexity. The resulting framework supports one-step and multi-step outcome prediction under specified treatment sequences. Experiments on CVSim and CancerSim demonstrate the superiority of HiSTOR across varying covariate dimensions, confounding strengths, and prediction horizons compared to state-of-the-art methods. To sum up, HiSTOR offers a unified approach to connecting high-dimensional covariate adjustment with longitudinal potential-outcome prediction.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.17739 [cs.LG]

(or arXiv:2610.17739v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.17739 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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