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cs.LG2025
LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
Po-Han Lee, Yu-Cheng Lin, Chan-Tung Ku +4
Estimating individualized treatment effects from observational data presents a persistent challenge due to unmeasured confounding and structural bias. Causal Machine Learning (caus…
cs.LG2025
Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
Peng-Yi Wu, Pei-Cing Huang, Ting-Yu Chen +3
Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recen…
cs.LG2024
Subgroup Analysis via Model-based Rule Forest
I-Ling Cheng, Chan Hsu, Chantung Ku +2
Machine learning models are often criticized for their black-box nature, raising concerns about their applicability in critical decision-making scenarios. Consequently, there is a…