用因果模型生成随时间变化的数据流，用于测试算法在数据漂移下的表现。
CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
- 基于因果模型动态调整特征与目标的关系，模拟数据分布和协变量漂移。
- 漂移后分类器准确率下降，随后逐步恢复，验证了生成效果。
- 适合研究数据漂移、在线学习或模型鲁棒性的研究人员使用。
本文提出一种基于结构因果模型（SCM）的时间依赖型合成数据生成框架——CaDrift。该框架可生成无限组合的具有可控漂移事件和时序特性的数据流，用于评估算法在动态数据环境下的表现。CaDrift通过改变SCM中映射函数来合成多种分布漂移与协变量漂移，从而改变特征与目标之间的潜在因果关系。此外，该框架利用因果干预建模偶然扰动。实验表明，分布漂移后分类器准确率下降，随后逐渐回升，验证了其模拟漂移的有效性。该框架已开源至GitHub。
原文摘要 · Abstract (English)
This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtually infinite combination of data streams with controlled shift events and time-dependent data, making it a tool to evaluate methods under evolving data. CaDrift synthesizes various distributional and covariate shifts by drifting mapping functions of the SCM, which change underlying cause-and-effect relationships between features and the target. In addition, CaDrift models occasional perturbations by leveraging interventions in causal modeling. Experimental results show that, after distributional shift events, the accuracy of classifiers tends to drop, followed by a gradual retrieval, confirming the generator's effectiveness in simulating shifts. The framework has been made available on GitHub.
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