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

Sep 10

Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes

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

safety

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 Computation and Language

Aug 28

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.

By Yucheng Zhou, Peng Luo, Qianning Wang, Chengzhong Xu, Jianbing Shen

llms reinforcement-learning multimodal benchmarks

One email a morning, machine-written

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Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes

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

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models