Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914551567482880 |
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| author | Lafrance, Julien Khoury, Richard Tremblay, Véronique |
| author_facet | Lafrance, Julien Khoury, Richard Tremblay, Véronique |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation Lafrance, Julien Khoury, Richard Tremblay, Véronique Machine Learning Artificial Intelligence 62H22, 62D20, 68T05 I.2.6; I.2.0; G.3 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. |
| title | Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation |
| topic | Machine Learning Artificial Intelligence 62H22, 62D20, 68T05 I.2.6; I.2.0; G.3 |
| url | https://arxiv.org/abs/2605.09663 |