面向含稀有事件的动态因果发现：一种非参数条件独立性检验
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
AI总结：
针对动态系统中仅在稀有低概率事件发生时显现的因果联系，提出基于时不变状态叠加数据的非参数条件独立性检验，给出非渐近样本复杂度界，并在模拟与PeMS真实事故数据上验证。
摘要：
与稀有事件相关的因果现象广泛存在于各类工程问题中，例如风险敏感的安全分析、事故分析与预防以及极值理论。然而，当前的因果发现方法往往无法揭示动态环境中随机变量之间仅在变量首次经历低概率实现时才显现的因果联系。为解决这一问题，我们针对从时不变动力系统中收集的数据提出了一种新颖的统计独立性检验，这类系统中会发生稀有但影响重大的事件。具体而言，我们利用底层数据的时不变性，构建了一个叠加数据集，其中包含不同时间步发生稀有事件之前的系统状态。随后，我们在重组后的数据上设计了一种条件独立性检验。我们为该方法的一致性提供了非渐近样本复杂度界，并在多种模拟和真实世界数据集上验证了其性能，包括从加州交通局绩效测量系统（PeMS）收集的事故数据。包含数据集和实验的代码已公开发布。
英文摘要：
Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.
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。
