8 papers
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
Ying Wu, Hanzhong Liu, Kai Ren +2
Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a…
From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
Yizhi Wang, Li Chen, Qiang Huang +24
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise,…
Regression adjustment in covariate-adaptive randomized experiments with missing covariates
Wanjia Fu, Yingying Ma, Hanzhong Liu
Covariate-adaptive randomization is widely used in clinical trials to balance prognostic factors, and regression adjustments are often adopted to further enhance the estimation and…
Minimax Optimal Design with Spillover and Carryover Effects
Haoyang Yu, Wei Ma, Hanzhong Liu
In various applications, the potential outcome of a unit may be influenced by the treatments received by other units, a phenomenon known as interference, as well as by prior treatm…
Incorporating external data for analyzing randomized clinical trials: A transfer learning approach
Yujia Gu, Hanzhong Liu, Wei Ma
Randomized clinical trials are the gold standard for analyzing treatment effects, but high costs and ethical concerns can limit recruitment, potentially leading to invalid inferenc…
Estimation and inference of average treatment effects under heterogeneous additive treatment effect model
Xin Lu, Hongzi Li, Hanzhong Liu
Randomized experiments are the gold standard for estimating treatment effects, yet network interference challenges the validity of traditional estimators by violating the stable un…