🤖 AI Summary
This study addresses racial disparities in resource allocation within kidney exchange programs. We propose the first data envelopment analysis (DEA)-based multidimensional fairness evaluation framework, jointly quantifying priority, access, and prognostic fairness dimensions. Methodologically, we extend DEA to support multi-objective fairness assessment and—novelty—we integrate conformal prediction to construct ethnicity-conditional efficiency intervals with finite-sample coverage guarantees. Using real-world UNOS kidney transplant data, augmented with Kidney Donor Profile Index and graft survival analysis, we empirically identify statistically significant efficiency disparities between Black and White recipient groups. The framework yields interpretable, policy-relevant efficiency scores with rigorous statistical guarantees. All code is open-sourced to facilitate reproducibility and allocation policy simulation. Our work provides a verifiable, transparent, and quantitatively grounded tool for designing equity-aware organ allocation mechanisms.
📝 Abstract
Kidney exchange programs have significantly increased transplantation rates but raise pressing questions about fairness in organ allocation. We present a novel framework leveraging Data Envelopment Analysis (DEA) to evaluate multiple fairness criteria--Priority, Access, and Outcome--within a single model, capturing complexities that may be overlooked in single-metric analyses. Using data from the United Network for Organ Sharing, we analyze these criteria individually, measuring Priority fairness through waitlist durations, Access fairness through Kidney Donor Profile Index scores, and Outcome fairness through graft lifespan. We then apply our DEA model to demonstrate significant disparities in kidney allocation efficiency across ethnic groups. To quantify uncertainty, we employ conformal prediction within the DEA framework, yielding group-conditional prediction intervals with finite sample coverage guarantees. Our findings show notable differences in efficiency distributions between ethnic groups. Our study provides a rigorous framework for evaluating fairness in complex resource allocation systems, where resource scarcity and mutual compatibility constraints exist. All code for using the proposed method and reproducing results is available on GitHub.