用因果模型模拟数据漂移，提前发现分类器潜在缺陷。
Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
- 构建数据生成过程的因果数字孪生，支持精准干预
- 在OSMH数据集上暴露传统方法无法检测的隐藏漏洞
- 适合关注模型鲁棒性与可解释性的工业落地团队
动态环境中机器学习分类器面临概念漂移——数据生成过程的变化导致性能下降。传统评估方法如静态测试集或噪声扰动无法保持表格数据中的因果依赖，常产生因果无效的评估结果。事后分析工具如SHAP和LIME仅提供相关性见解，未必反映导致模型失效的因果机制。本文提出一种框架，利用结构因果模型作为数据生成过程的“数字孪生”，在保留结构依赖的同时实现精确的因果干预。所提技术——因果参数漂移模拟，可对分类器进行压力测试，识别部署前的脆弱点。在开放精神疾病数据集（OSMH）上的实验表明，该方法揭示了标准统计监控无法察觉的潜在缺陷。
原文摘要 · Abstract (English)
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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