因果参数漂移模拟：一种用于分类器鲁棒性评估的数字孪生框架
Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
AI总结：
本文提出一种基于结构因果模型的数字孪生框架，用于评估分类器在动态环境中的鲁棒性，通过因果干预揭示潜在漏洞。
摘要：
机器学习分类器在动态环境中面临概念漂移——数据生成过程的变化会降低性能。传统通过静态测试集或噪声扰动进行评估的方法无法保持表格数据中的因果依赖关系，往往产生因果无效的评估。后验工具如SHAP和LIME提供相关性见解，但可能无法反映驱动模型失败的因果机制。我们提出一种框架，通过将结构因果模型作为数据生成过程的“数字孪生”，补充现有漂移检测方法，实现精确的因果干预并保持结构依赖性。我们的技术，因果参数漂移模拟，通过压力测试分类器以在部署前识别其漏洞。在开放源码精神疾病（OSMH）数据集上的实验表明，该方法揭示了标准统计监控无法发现的潜在漏洞。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
