因果时间机制结构学习
Causal Temporal Regime Structure Learning
AI总结：
针对非平稳、分段变化的多元时间序列，提出 CASTOR 联合识别机制边界、数量、顺序及各机制 DAG，并证明可辨识性且实验表现更优。
摘要：
理解多元时间序列中的因果关系，对于预测和控制经济学、神经科学和气候科学等领域的动态系统至关重要。然而，现有因果发现方法通常假设平稳性；当时间序列由连续机制（即边界未知且因果结构不断变化的连续时间段）组成时，这些方法的有效性会受到限制。在本工作中，我们首先提出一个框架来描述和建模此类时间序列。随后，我们提出 CASTOR，这是一种新颖方法，可同时学习每个机制的有向无环图（Directed Acyclic Graph，DAG），并确定机制数量及其顺序排列。CASTOR 使用期望最大化算法优化数据对数似然，在分配机制索引（期望步）与推断每个机制中的因果关系（最大化步）之间交替进行。我们证明了该框架内机制和 DAG 的可辨识性。大量实验表明，在合成数据集和真实世界数据集上，在线性和非线性因果关系等多种设置中，CASTOR 在检测不同机制并学习其 DAG 方面始终优于现有因果发现模型。
英文摘要：
Understanding causal relationships in multivariate time series is essential for predicting and controlling dynamic systems in fields like economics, neuroscience, and climate science. However, existing causal discovery methods often assume stationarity, limiting their effectiveness when time series consist of sequential regimes, consecutive temporal segments with unknown boundaries and changing causal structures. In this work, we firstly introduce a framework to describe and model such time series. Then, we present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime while determining the number of regimes and their sequential arrangement. CASTOR optimizes the data log-likelihood using an expectation-maximization algorithm, alternating between assigning regime indices (expectation step) and inferring causal relationships in each regime (maximization step). We establish the identifiability of the regimes and DAGs within our framework. Extensive experiments show that CASTOR consistently outperforms existing causal discovery models in detecting different regimes and learning their DAGs across various settings, including linear and nonlinear causal relationships, on both synthetic and real world datasets.
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。
