Selected work

Representative Papers

Application of Propensity Score Models and Causal Estimators in Observational Studies under Model Misspecification

May 19, 2026

This study systematically evaluates the robustness of response surface modeling (RSM), inverse probability weighting (IPW), and augmented inverse probability weighting (AIPW) under various misspecification scenarios of propensity score and outcome models in observational studies. Leveraging multiple methods—including logistic regression, random forests, support vector machines, and linear discriminant analysis—to estimate propensity scores, the authors assess performance through extensive simulations and real-world applications to the ACTG175 and ADNI datasets. Findings indicate that AIPW demonstrates consistent robustness across most settings, benefiting from its double-robustness property; IPW proves highly sensitive to propensity score misspecification, while RSM performs well only when the outcome model is correctly specified. The results underscore that integrating flexible machine learning techniques within a doubly robust framework substantially enhances the reliability of causal effect estimation.

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A Tutorial for Evaluating Cure Model Appropriateness

May 06, 2026

In survival analysis, traditional models assume all individuals will eventually experience the event of interest. However, advances in therapeutics have led to multiple clinical contexts with potentially curative therapies, and in these contexts, certain individuals may never experience the event. Statisticians have developed cure models as a methodology to address this challenge. Nonetheless, despite significant statistical advances in cure models, we have seen more limited uptake in biomedical applications, and we hypothesize that this is caused by limited guidance in the appropriate application of cure models. Cure models require specific identifiability conditions for valid parameter estimation, and previous reports have demonstrated significant issues with the inappropriate application of cure models. Existing tutorials for cure models focus on model implementation and either assume or provide only limited guidance on whether cure modeling is appropriate for the given dataset. This tutorial addresses this gap by describing a systematic procedure that integrates clinical judgment, visual inspection of Kaplan-Meier curves, and quantitative evaluation. We provide a worked example using data from a randomized clinical trial in acute myeloid leukemia, and we also summarize findings from a series of other datasets of hematopoietic cell transplantation to suggest broad practical guidance for choosing to apply cure models. By systematically evaluating cure model appropriateness before fitting these models, researchers can achieve more reliable survival analysis and improved clinical decision-making.

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FORCE: Feature-Oriented Representation with Clustering and Explanation

Apr 07, 2025

This paper addresses the insufficient modeling of latent variables in representation learning. We propose an interpretable deep learning framework that explicitly captures the influence of unobserved factors on feature importance via two-stage SHAP value integration. First, SHAP values serve as supervision signals to guide K-means clustering, yielding semantically interpretable latent features. Second, SHAP values are embedded into a learnable attention mechanism to enable dynamic modulation of feature weights by latent structure. To our knowledge, this is the first work to directly transform model-agnostic explanation scores into end-to-end differentiable architectural components, jointly achieving interpretability and discriminative power. Extensive experiments on three real-world healthcare datasets demonstrate efficacy: e.g., F1-score for heart disease prediction reaches 0.80—improving over baselines by 0.08—validating that synergistic integration of latent features and attention significantly enhances performance.

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Recent publications

Latest Papers

Application of Propensity Score Models and Causal Estimators in Observational Studies under Model Misspecification

May 19, 2026

This study systematically evaluates the robustness of response surface modeling (RSM), inverse probability weighting (IPW), and augmented inverse probability weighting (AIPW) under various misspecification scenarios of propensity score and outcome models in observational studies. Leveraging multiple methods—including logistic regression, random forests, support vector machines, and linear discriminant analysis—to estimate propensity scores, the authors assess performance through extensive simulations and real-world applications to the ACTG175 and ADNI datasets. Findings indicate that AIPW demonstrates consistent robustness across most settings, benefiting from its double-robustness property; IPW proves highly sensitive to propensity score misspecification, while RSM performs well only when the outcome model is correctly specified. The results underscore that integrating flexible machine learning techniques within a doubly robust framework substantially enhances the reliability of causal effect estimation.

0 citationsRead paper

A Tutorial for Evaluating Cure Model Appropriateness

May 06, 2026

In survival analysis, traditional models assume all individuals will eventually experience the event of interest. However, advances in therapeutics have led to multiple clinical contexts with potentially curative therapies, and in these contexts, certain individuals may never experience the event. Statisticians have developed cure models as a methodology to address this challenge. Nonetheless, despite significant statistical advances in cure models, we have seen more limited uptake in biomedical applications, and we hypothesize that this is caused by limited guidance in the appropriate application of cure models. Cure models require specific identifiability conditions for valid parameter estimation, and previous reports have demonstrated significant issues with the inappropriate application of cure models. Existing tutorials for cure models focus on model implementation and either assume or provide only limited guidance on whether cure modeling is appropriate for the given dataset. This tutorial addresses this gap by describing a systematic procedure that integrates clinical judgment, visual inspection of Kaplan-Meier curves, and quantitative evaluation. We provide a worked example using data from a randomized clinical trial in acute myeloid leukemia, and we also summarize findings from a series of other datasets of hematopoietic cell transplantation to suggest broad practical guidance for choosing to apply cure models. By systematically evaluating cure model appropriateness before fitting these models, researchers can achieve more reliable survival analysis and improved clinical decision-making.

0 citationsRead paper

FORCE: Feature-Oriented Representation with Clustering and Explanation

Apr 07, 2025

This paper addresses the insufficient modeling of latent variables in representation learning. We propose an interpretable deep learning framework that explicitly captures the influence of unobserved factors on feature importance via two-stage SHAP value integration. First, SHAP values serve as supervision signals to guide K-means clustering, yielding semantically interpretable latent features. Second, SHAP values are embedded into a learnable attention mechanism to enable dynamic modulation of feature weights by latent structure. To our knowledge, this is the first work to directly transform model-agnostic explanation scores into end-to-end differentiable architectural components, jointly achieving interpretability and discriminative power. Extensive experiments on three real-world healthcare datasets demonstrate efficacy: e.g., F1-score for heart disease prediction reaches 0.80—improving over baselines by 0.08—validating that synergistic integration of latent features and attention significantly enhances performance.

0 citationsRead paper