Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect

2025-11-18

AI Summary

In clinical machine learning, the Rashomon effect—where multiple models achieve similar predictive performance—combined with small sample sizes, class imbalance, high noise, and weakly discriminative features, undermines conventional validation and introduces uncertainty in model selection. To address this, we propose an intervention-efficiency–guided perturbation validation framework: (1) We introduce a capacity-aware intervention efficiency metric that explicitly incorporates clinical intervention capability and resource constraints into utility evaluation; (2) We develop a systematic data perturbation validation framework, enhancing robustness through stability analysis on both synthetic and real-world healthcare datasets. Experiments demonstrate that our approach significantly improves model generalization under distributional shift and strengthens clinical applicability. The resulting model selection process achieves a principled balance among practical utility, robustness, and interpretability—enabling trustworthy deployment in real-world clinical settings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In clinical machine learning, the coexistence of multiple models with comparable performance -- a manifestation of the Rashomon Effect -- poses fundamental challenges for trustworthy deployment and evaluation. Small, imbalanced, and noisy datasets, coupled with high-dimensional and weakly identified clinical features, amplify this multiplicity and make conventional validation schemes unreliable. As a result, selecting among equally performing models becomes uncertain, particularly when resource constraints and operational priorities are not considered by conventional metrics like F1 score. To address these issues, we propose two complementary tools for robust model assessment and selection: Intervention Efficiency (IE) and the Perturbation Validation Framework (PVF). IE is a capacity-aware metric that quantifies how efficiently a model identifies actionable true positives when only limited interventions are feasible, thereby linking predictive performance with clinical utility. PVF introduces a structured approach to assess the stability of models under data perturbations, identifying models whose performance remains most invariant across noisy or shifted validation sets. Empirical results on synthetic and real-world healthcare datasets show that using these tools facilitates the selection of models that generalize more robustly and align with capacity constraints, offering a new direction for tackling the Rashomon Effect in clinical settings.

Problem

• Selecting equally performing clinical models under Rashomon Effect uncertainty
• Addressing unreliable validation from imbalanced noisy clinical datasets
• Aligning model selection with intervention capacity and clinical priorities

Innovation

• Intervention Efficiency metric links prediction with clinical utility
• Perturbation Validation Framework assesses model stability under data changes
• Capacity-aware selection method addresses Rashomon Effect in clinical models