CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
CausalCompass：模型错设情景下时间序列因果发现的稳健性评估AI 解读 ↓
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CausalCompass：模型错设情景下时间序列因果发现的稳健性评估
- 研究问题
- 时间序列因果发现依赖的建模假设遭到违反时，不同算法的表现如何变化，怎样建立能够系统评估稳健性而非只比较理想设定的基准？
- 方法与思路
- 作者提出可扩展评估框架，在八类假设违反情景下比较代表性算法，并开展超参数敏感性分析、消融实验及标准化预处理比较。
- 材料陈述的贡献
- 摘要称，没有算法在所有设定下始终最优，深度学习方法总体表现较突出；NTS-NOTEARS 的表现对标准化预处理高度敏感。
- 阅读关注点
- 应核对八类情景的生成机制、评价指标、调参与计算预算是否可比，以及深度学习和标准化优势是否依赖特定数据设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验。摘要未给出情景细节或数值，所述表现不能推广到全部真实时间序列；链接未访问。
原文摘要
Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the lack of robustness-oriented evaluation in existing benchmarks. To address these challenges, we propose CausalCompass, a flexible and extensible benchmark framework designed to assess the robustness of time-series causal discovery (TSCD) methods under violations of modeling assumptions. To demonstrate the practical utility of CausalCompass, we conduct extensive benchmarking of representative TSCD algorithms across eight assumption-violation scenarios. Our experimental results indicate that no single method consistently attains optimal performance across all settings. Nevertheless, the methods exhibiting superior overall performance across diverse scenarios are almost invariably deep learning-based approaches. We further provide hyperparameter sensitivity analyses to deepen the understanding of these findings. We additionally conduct ablation experiments to explain the strong performance of deep learning-based methods under assumption violations. We also find, somewhat surprisingly, that NTS-NOTEARS relies heavily on standardized preprocessing in practice, performing poorly in the vanilla setting but exhibiting strong performance after standardization. Finally, our work aims to provide a comprehensive and systematic evaluation of TSCD methods under assumption violations, thereby facilitating their broader adoption in real-world applications. The user-friendly implementation, documentation and datasets are available at https://anonymous.4open.science/r/CausalCompass-anonymous-5B4F/.