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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.\n  We propose a framework that complements existing drift detection by leveraging Structural Causal Models as \"Digital Twins\" of data-generating processes, enabling precise causal interventions while preserving structural dependencies. Our technique, Causal Parametric Drift Simulation, stress-tests classifiers to identify vulnerabilities before deployment. Experiments on the Open Sourcing Mental Illness (OSMH) dataset demonstrate that this approach exposes latent vulnerabilities invisible to standard statis","title":"Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation","url":"https://arxiv.org/abs/2605.09663","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.09663v1 Announce Type: cross \nAbstract: 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.\n  We propose a framework that complements existing drift detection by leveraging Structural Causal Models as \"Digital Twins\" of data-generating processes, enabling precise causal interventions while preserving structural dependencies. Our technique, Causal Parametric Drift Simulation, stress-tests classifiers to identify vulnerabilities before deployment. 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