7 papers
InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI
Can Wang, Hongyu Zhao, Yiqun Chen
Causal inference is central to scientific discovery, yet choosing appropriate methods remains challenging because of the complexity of both statistical methodology and real-world d…
Power Analysis for Prediction-Powered Inference
Yiqun T. Chen, Moran Guo, Shengy Li
Modern studies increasingly leverage outcomes predicted by machine learning and artificial intelligence (AI/ML) models, and recent work, such as prediction-powered inference (PPI),…
Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins
Jasmin Han, Janardan Devkota, Joseph Waring +8
Perceived message effectiveness (PME) by potential intervention end-users is important for selecting and optimizing personalized smoking cessation intervention messages for mobile…
Efficient Inference for Noisy LLM-as-a-Judge Evaluation
Yiqun T Chen, Sizhu Lu, Sijia Li +2
Large language models (LLMs) are increasingly used as automatic evaluators of generative AI outputs, a paradigm often referred to as "LLM-as-a-judge." In practice, LLM judges are i…
Large Language Models for Full-Text Methods Assessment: A Case Study on Mediation Analysis
Wenqing Zhang, Trang Nguyen, Elizabeth A. Stuart +1
Systematic reviews are crucial for synthesizing scientific evidence but remain labor-intensive, especially when extracting detailed methodological information. Large language model…
Evaluating Large Language Models for Evidence-Based Clinical Question Answering
Can Wang, Yiqun Chen
Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evid…