arXiv Machine Learning

Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

arXiv:2606. 12623v2 Announce Type: replace-cross Abstract: Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions.

arXiv AI

Aug 12

Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement

arXiv:2608. 10339v1 Announce Type: cross Abstract: Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal effects of such interventions.

By Patrick Vossler, Jialin Ouyang, F. Richard Guo, Anran Huang, Ali Shojaie, Lucas Zier, Fan Xia, Jean Feng

llms safety

arXiv AI

Aug 7

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

arXiv:2608. 05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning.

By Esra Zihni, Katryna Cisek, Hamzah Ziadeh, Hendrik Knoche, Robert Mikulik, John D. Kelleher

arXiv Machine Learning

Jun 25

Enhancing Clinician Decision-Making via Uncertainty-Aware Multi-Expert Fusion for Stroke Rehabilitation

arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.

By Tamim Ahmed, Thanassis Rikakis

multimodal

arXiv AI

Jun 15

Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?

arXiv:2604. 14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators.

By Amy Rouillard, Sitwala Mundia, Linda Camara, Ziyaad Dangor, Michael Cameron Gramanie, Ismail Kalla, Shabir A. Madhi, Kajal Morar, Marlvin T. Ncube, Haroon Saloojee, Bruce A. Bassett

llms agents benchmarks safety

arXiv Machine Learning

Jul 27

LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies

arXiv:2604. 16763v3 Announce Type: replace Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data.

By Lei Liu, Jialin Chen, Kathy Macropol

llms nlp safety

arXiv AI

Jul 17

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.

By Yichen Xu

llms diffusion benchmarks safety

arXiv AI

Jul 29

Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records

arXiv:2507. 20993v4 Announce Type: replace-cross Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text.

By Henri Arno, Thomas Demeester

multimodal

arXiv Machine Learning

Jul 7

Coverage-Controlled Preference Mining from Noisy Claim Verification for Evidence-Grounded Generation

arXiv:2603. 10494v2 Announce Type: replace-cross Abstract: Evidence-grounded generation produces summaries whose claims should be supported by supplied evidence, but claim-level verifiers provide noisy feedback and can reward models that simply say less.

By Weixin Liu, Congning Ni, Qingyuan Song, Susannah L. Rose, Murat Kantarcioglu, Bradley A. Malin, Zhijun Yin

llms rag nlp reinforcement-learning

arXiv Machine Learning

Jun 17

The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance

arXiv:2606. 17113v1 Announce Type: new Abstract: Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance.

By Csaba Kiss, Roland Molontay, Gabriele Pergola

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 28

TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure

arXiv:2607. 22762v1 Announce Type: cross Abstract: Causal inference has become a central issue across various fields, including computer science, statistics, economics, education, healthcare, and medicine.

By Ali Haghpanah Jahromi, Mohammad Taheri, Zohreh Azimifar

benchmarks

arXiv Machine Learning

Aug 7

A Unified Causal Inference Framework for the Desirability of Outcome Ranking Paradigm in Benefit-Risk Evaluation

arXiv:2608. 05244v1 Announce Type: cross Abstract: We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies.

By Yuan Feng, Shiyu Shu, Yixin Fang, Ionut Bebu, Toshimitsu Hamasaki, Scott Evans, Guoqing Diao

safety

arXiv AI

Jun 15

Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation

arXiv:2505. 17961v4 Announce Type: replace-cross Abstract: Causal inference typically assumes centralized access to individual-level data.

By R\'emi Khellaf, Aur\'elien Bellet, Julie Josse

safety

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