arXiv Machine Learning

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.

Hugging Face Trending Papers

Jul 29

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

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.

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 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 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 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

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 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

Aug 25

Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments

arXiv:2608.22024v1 Announce Type: cross Abstract: Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decis...

By Yuki Murakami, Takumi Hattori, Kohsuke Kubota

benchmarks safety

arXiv Machine Learning

Jun 5

Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction

arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.

By Amirhossein Zare, Amirhessam Zare, Herlock Rahimi, Reza Salarikia, Mohammad Kashkooli

benchmarks

arXiv Machine Learning

Aug 11

Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects

arXiv:2608. 08288v1 Announce Type: new Abstract: Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support.

By Abisoye Abidakun, Mingjun Zhong, Georgios Leontidis

llms safety

arXiv Machine Learning

Aug 4

Causal Inference with Unstructured Treatments

arXiv:2608. 00657v1 Announce Type: cross Abstract: Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions.

By Kevin Christian Wibisono, Yixin Wang

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