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: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
The paper proposes a method for estimating treatment effects using AI-generated predictions as surrogates, applied to paired before-and-after measurements for each treated individual. By comparing AI predictions before and after treatment, the approach can identify the average treatment effect on the treated under certain technical assumptions, even when clinical outcomes are never observed for treated subjects. When assumptions are questionable, the authors introduce prediction‑powered inference that corrects bias with a small set of observed outcomes, and validate the method with synthetic and cardio‑oncology data.
By Frances Dean, Anna Neufeld, Joshua Barrios, Geoffrey H Tison, Ahmed Alaa
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
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
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
The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
arXiv:2607. 18999v1 Announce Type: cross Abstract: Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is sufficient.
By Guofeng Zhang, Yizeng Quan, Huaiyi Fang, Jianwei Lv, Jinyao Liu, Xunxu Duan, Lening An, Yu Ouyang, Junfeng Wang
The article introduces MedGPT Clinical Explorer (MCE), a conditional‑strategy framework designed to aid complex lung cancer decision‑making by explicitly mapping patient conditions to pathway eligibility, deferral, and redirection. In a study with 250 physicians across 98 institutions, MCE‑assisted strategies achieved higher Admissible Pathway Attainment Scores (APAS) than unaided or retrieval‑reference approaches, indicating more comprehensive inclusion of clinically relevant content and coherent links among pathways, conditions, and actions. The authors suggest that MCE’s shared decision object could improve transparency of omissions and contingencies, warranting prospective evaluation of its impact on workflow and patient outcomes.
By Daoyun Wang, Zhicheng Huang, Huaiyuan Sun, Jiaqi Xu, Xiaowei Xu, Zhibo Zheng, Zhongxing Bing, Yuxiao Lin, Yicheng Liang, Chao Gao, Bowen Xue, Kai Zhang, Song Xu, Wanpu Yan, Hui Xia, Lin Li, Xiang Yan, Mu Hu, Qianli Ma, Zhiqiang Xue, Xiaofang Liu, Zhihai Han, Nan Zhang, Chuanhao Tang, Tongmei Zhang, Lan Song, Zhaohui Zhu, Xuan Zeng, Shafei Wu, Hui Guan, Lei Deng, Huaxia Yang, Zeliang Lian, Wubin Sun, Yongxin Wang, Xiaohui Shen, Binlin Wang, Tiantian Gu, Yu Cui, Li Zhang, Shirui Wang, Naixin Liang
arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.
By Aakash Bhagat, Shashank Choudhary
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.
By Florent Pollet, Matthew McDermott
arXiv:2609.06294v1 Announce Type: new
Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
By Maitreyi Swaroop, Shikha Bhat, Samantha Rodriguez, Tamar Krishnamurti, Bryan Wilder