14 citations · 14 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
Chih-Yuan Li, Jun-Ting Wu, Chan Hsu +2
The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of kidney function in clinical practice. Although traditional equations and Machine Learning (ML) models u…
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…
Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation
Chan Hsu, Jun-Ting Wu, Yihuang Kang
Understanding and inferencing Heterogeneous Treatment Effects (HTE) and Conditional Average Treatment Effects (CATE) are vital for developing personalized treatment recommendations…