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

• URL: http://arxiv.org/abs/2605.09663v1
• Date: Sun, 10 May 2026 17:28:49 GMT
• Title: Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
• Abstract summary: Machine learning classifiers in dynamic environments face concept drift.<n>We propose a framework that complements existing drift detection by leveraging Structural Causal Models as "Digital Twins"<n>Our technique, Causal Parametric Drift Simulation, stress-tests classifiers to identify vulnerabilities before deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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. 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 statistical monitors.

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