Enhancing Counterfactual Explanations with Feasibility and Diversity
Abstract
This paper studies how to improve counterfactual explanations with feasibility and diversity constraints.
X. Qin, S. Li, Y. Cai, and L. Wang. Enhancing Counterfactual Explanations with Feasibility and Diversity. In Proceedings of the 2025 IEEE International Conference on Data Mining Workshops (ICDMW 2025), 2025.
Xinyu Qin
PhD Student of Biomedical Engineering
My research interests include Causal Inference, Survival Analysis, and Digital Twin.
Lu Wang
Assistant Professor of Biomedical Engineering
My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.