The Role of Sensitivity Analysis in Evaluating Model Robustness and Identifying Key Statistical Drivers

Posted: Feb 04, 2004

• Sarah Torres
• Liam Thompson
• Matthew Green

Abstract

The proliferation of complex statistical models across scientific and industrial domains has created an urgent need for robust evaluation methodologies that extend beyond conventional performance metrics. While predictive accuracy remains a primary concern, the stability, reliability, and interpretability of model behavior under varying conditions represent equally critical considerations. Sensitivity analysis, traditionally employed as a supplementary diagnostic tool, offers untapped potential for addressing these challenges when reconceptualized as a central component of model development and validation. This research introduces a transformative framework that elevates sensitivity analysis from its peripheral status to a foundational methodology for comprehensive model assessment. Contemporary machine learning and statistical modeling practices often prioritize optimization of point estimates while neglecting systematic investigation of how models respond to perturbations in their inputs, parameters, and structural assumptions. This oversight becomes particularly problematic when models are deployed in high-stakes environments where understanding failure modes and identifying critical drivers can have substantial real-world consequences. The financial crisis of 2008, for instance, demonstrated how inadequate sensitivity testing of risk models could lead to catastrophic systemic failures. Similarly, in healthcare applications, models that perform well on average may exhibit dangerous instability for specific patient subgroups, highlighting the need for more nuanced evaluation approaches. Our work addresses these limitations through three interconnected innovations. First, we develop a topological sensitivity measure that captures how model predictions vary with respect to the underlying data manifold structure, moving beyond traditional point-wise sensitivity calculations. Second, we introduce cross-domain sensitivity transfer, a methodology that enables robustness insights gained in one problem domain to inform model development in another, creating a knowledge-sharing mechanism for sensitivity patterns. Third, we implement dynamic sensitivity tracking that monitors how sensitivity profiles evolve throughout the training process, providing unprecedented visibility