Hugging Face Trending Papers July 29, 2026

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

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Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the conditional average treatment effect (CATE), a standard estimand for characterizing such heterogeneity.

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arXiv Machine Learning

Jul 30

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

arXiv:2607. 26599v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine.

By Jialu Xu, Mengkun Liang, Guannan Liu, Xiaojie Mao, Junjie Wu

llms benchmarks

arXiv Machine Learning

Jul 10

Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment

arXiv:2603. 19186v3 Announce Type: replace Abstract: Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity.

By Amir Asiaee, Samhita Pal

rag 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 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 2

CausalMix: Data Mixture as Causal Inference for Language Model Training

arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.

By Zinan Tang, Yukun Zhang, Shaomian Zheng, Zhuoshi Pan, Qizhi Pei, Dingnan Jin, Jun Zhou, Yujun Wang, Biqing Huang

llms

arXiv Machine Learning

Jul 1

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.

By Wooseok Ha, Yuansi Chen

fine-tuning

Related stories

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment

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

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

CausalMix: Data Mixture as Causal Inference for Language Model Training

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts