Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
For practitioners deploying classifiers in dynamic environments, this provides a causally valid stress-testing method to identify failure points before deployment.
The paper proposes a framework using Structural Causal Models as digital twins to simulate causal parametric drift for evaluating classifier robustness, demonstrating on the OSMH dataset that it exposes vulnerabilities missed by standard methods.
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.