Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation

ArXi:2605.09663v1 Announce Type: cross Machine learning classifiers in dynamic environments face concept drift -- changes in the data-generating process that degrade performance. Conventional evaluation via static test sets or noise perturbations fails to preserve causal dependencies in tabular data, often producing causally invalid assessments. Post-hoc tools like SHAP and LIME offer correlational insights that may not reflect the causal mechanisms driving model failure.