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

Julien Lafrance; Richard Khoury; Véronique Tremblay

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arXiv · 2605.09663

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

Julien Lafrance·Richard Khoury·Véronique Tremblay

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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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Julien Lafrance, Richard Khoury, Véronique Tremblay. 2026-05-10. Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation. https://arxiv.org/abs/2605.09663

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