论文标题
迈向罕见事件的动态因果发现：非参数有条件独立测试
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
论文作者
论文摘要
与罕见事件相关的因果现象发生在广泛的工程问题上，例如对风险敏感的安全分析，事故分析和预防以及极值理论。但是，当前的因果发现方法通常无法发现动态设置中随机变量之间的因果链接，仅当变量首先经历低概率实现时才表现出来。为了解决这个问题，我们对从发生时间不变的动态系统收集的数据引入了新的统计独立性测试，其中发生了罕见但因此发生的事件。特别是，我们利用基本数据的时间传不同来构建系统状态的叠加数据集，然后再在不同的时间段上发生罕见事件。然后，我们对重组数据设计有条件的独立测试。我们为我们的方法的一致性提供了非反应样品复杂性界限，并在各种模拟和实际数据集中验证其性能，包括从CalTrans性能测量系统（PEMS）收集的事件数据。包含数据集和实验的代码已公开可用。
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.
