Sensitivity Analysis
Overview
Sensitivity analysis asks which inputs, assumptions, or model components drive an output, and how strongly their effects change across conditions. Common approaches include controlled perturbations, derivative and adjoint methods, variance-based measures such as Sobol indices, Shapley effects, and surrogate models for costly simulations. These tools are used to understand physical and agent-based models, assess causal conclusions, prioritize features, and audit machine-learning reliability and robustness. A central challenge is making analysis scalable while distinguishing sensitivity from causality or correctness: accurate predictions and strong average scores do not guarantee credible responses to specific changes.
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