5 papers
Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
Byeonghee Lee, Sangwook Kang, Ju-Hyun Park +2
We propose a doubly robust estimator for the average treatment effect in high dimensional low sample size observational studies, where contamination and model misspecification pose…
Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood
Byeonghee Lee, Joonsung Kang
This paper develops a unified framework for estimating continuous outcomes under multiple treatment levels in observational studies. We integrate the Generalized Propensity Score (…
Outlier-Resistant Heterogeneous Treatment Effect Estimation in HDLSS Settings via GAT--CVAE Framework
Byeonghee Lee, Joonsung Kang
We introduce a robust framework for heterogeneous treatment effect (HTE) estimation tailored to high-dimensional low sample size (HDLSS) settings. By combining Graph Attention Netw…
Doubly robust average treatment effect estimation for survival data
Byeonghee Lee, Joonsung Kang
Considering censored outcomes in survival analysis can lead to quite complex results in the model setting of causal inference. Causal inference has attracted a lot of attention ove…
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Byeonghee Lee, Joonsung Kang
Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We…