Contribution-driven Data Envelopment Analysis Ranking Via Shapley Values and Hierarchical Frontiers

To address the critical limitation of traditional data envelopment analysis (DEA) in discriminating among efficient decision-making units (DMUs), this study introduces a novel contribution-driven ranking framework integrating Shapley values and hierarchical frontier analysis. An interactive contribution analysis framework is developed to systematically quantify the marginal impacts of efficient DMUs on the efficiency fluctuations of inefficient ones via Shapley values, overcoming the single-unit removal bias and modeling multivariate combinatorial interactions. A dynamic hierarchical frontier stripping system is designed to reconstruct efficiency frontiers by iteratively excluding evaluated DMUs, incorporating potential benchmark relationships to enable comprehensive discrimination of all DMUs. The proposed method establishes a bidirectional interaction mechanism between efficient and inefficient DMUs, inferring the structural contributions of efficient DMUs to the frontier through the efficiency fluctuations of inefficient DMUs. This approach significantly enhances the discrimination capability for both unreferenced efficient DMUs and inefficient DMUs. Empirical validations across two cases demonstrate that the framework provides a data-driven methodological advancement for DEA ranking, seamlessly integrating with traditional efficiency evaluations to offer scientific decision support for resource optimization and benchmark analysis in DEA contexts.