arXiv Machine Learning By Mengfei Ran, Yifeng Shen, Ruijie Guan

Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories

Read the original on arXiv Machine Learning →

arXiv:2607. 28567v1 Announce Type: cross Abstract: Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Sep 17

Information Set Emulation: Causal Certificates for AI Derived EHR Features

The paper introduces information set emulation, a method that attaches detailed causal certificates—such as source evidence, timing, and proposed causal roles—to AI‑derived features extracted from electronic health records (EHRs). These certificates provide auditable evidence for causal roles and guide whether a feature can be used for causal inference or should be routed to compatible reporting or separate analyses. The framework integrates with a joint EHR observation map and offers identification, estimation, and diagnostic tools under standard causal assumptions, illustrated through synthetic simulations and a finite‑world example.

By Takes Fujita (VRI), Nobutaka Hattori (Department of Neurology, Juntendo University School of Medicine)
arXiv AI
Jul 22

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.

By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud