arXiv subjects

Byeonghee Lee

Publications and source records attributed to Byeonghee Lee.

5 recordsLinked to original sources

arXiv2025

Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood

This paper develops a unified framework for estimating continuous outcomes under multiple treatment levels in observational studies. We integrate the Generalized Propensity Score (GPS), Covariate Balancing Propensity Score (CBPS), and outcome regression into a Penalized Empirical Likelihood (PEL) formulation. The GPS is parameterized by $\boldsymbol{\beta}$ and denoted $\pi_{\boldsymbol{\beta}}(\mathbf{X})$, while CBPS imposes moment conditions to ensure covariate balance. Outcome regression flexibly models the continuous response $Y$, and doubly robust estimation ensures consistency under either correct model specification. PEL allows simultaneous estimation and variable selection using general estimating equations. Simulation results and comparisons with state-of-the-art meta-learners confirm the effectiveness of our method.

stat.ME↗

arXiv2025

Outlier-Resistant Heterogeneous Treatment Effect Estimation in HDLSS Settings via GAT--CVAE Framework

We introduce a robust framework for heterogeneous treatment effect (HTE) estimation tailored to high-dimensional low sample size (HDLSS) settings. By combining Graph Attention Networks (GAT) to capture structural dependencies among confounders with a Conditional Variational Autoencoder (CVAE) for latent representation learning, our method expands the sample space and performs clustering that integrates even outlier sets into coherent subgroups. Clusterwise causal effects are then estimated using a doubly robust outlier-resistant estimator, yielding stable and generalizable results. Simulations and real-world applications confirm superior performance compared with existing HTE methods, highlighting the framework's potential for precision medicine and policy evaluation.

stat.ME↗

arXiv2025

Doubly robust average treatment effect estimation for survival data

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 over the past few years, but little research has been done on survival analysis. Even for the only research conducted, the machine learning method was considered assuming a large sample, which is not suitable in that the actual data are high dimensional low sample size (HDLSS) method. Therefore, penalty is considered for numerous covariates, and the relationship between these covariates and treatment variables is reflected as a covariate balancing property score (CBPS). It also considers censored results. To this end, we will try to solve the above-mentioned problems by using penalized empirical likelihood, which considers both estimating equation and penalty. The proposed average treatment effect (ATE) estimator possesses the oracle property, exhibiting key characteristics such as double robustness for unbiasedness, sparsity in model selection, and asymptotic normality.

stat.ME↗

arXiv2025

An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions

Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate performance using both simulated multi-omics data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our method demonstrates superior predictive accuracy, feature selection precision, and biological relevance. Biomarker gene maps derived from both datasets are visualized and interpreted, offering insights into latent molecular mechanisms underlying dementia.

eess.IV↗

arXiv2025

Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions

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 serious inferential challenges. The estimator combines bounded influence estimating equations for outcome modeling with covariate balancing propensity scores for treatment assignment, embedded within a penalized empirical likelihood framework using nonconvex regularization. It satisfies the oracle property by jointly achieving consistency under partial model correct ness, selection consistency, robustness to contamination, and asymptotic normality. For uncertainty quantification, we derive a finite sample confidence interval using cumulant generating functions and influence function corrections, avoiding reliance on asymptotic approximations. Simulation studies and applications to gene expression datasets (Golub and Khan) demonstrate superior performance in bias, error metrics, and interval calibration, highlighting the method robustness and inferential validity in HDLSS regimes. One notable aspect is that even in the absence of contamination, the proposed estimator and its confidence interval remain efficient compared to those of competing models.

stat.ME↗