Discovering causal relationships in time series data is central in many scientific areas, ranging from economics to climate science. Granger causality is a powerful tool for causality detection. However, its original formulation is limited by its linear form and only recently nonlinear machine-learning generalizations have been introduced. This study contributes to the definition of neural Granger causality models by investigating the application of Kolmogorov-Arnold networks (KANs) in Granger causality detection and comparing their capabilities against multilayer perceptrons (MLP). In this work, we develop a framework called Granger Causality KAN (GC-KAN) along with a tailored training approach designed specifically for Granger causality detection. We test this framework on both Vector Autoregressive (VAR) models and chaotic Lorenz-96 systems, analysing the ability of KANs to sparsify input features by identifying Granger causal relationships, providing a concise yet accurate model for Granger causality detection. Our findings show the potential of KANs to outperform MLPs in discerning interpretable Granger causal relationships, particularly for the ability of identifying sparse Granger causality patterns in high-dimensional settings, and more generally, the potential of AI in causality discovery for the dynamical laws in physical systems.

翻译：在从经济学到气候科学的众多科学领域中，发现时间序列数据中的因果关系至关重要。格兰杰因果关系是进行因果关系检测的有力工具。然而，其原始形式受限于线性假设，直到最近才引入了非线性的机器学习推广形式。本研究通过探究Kolmogorov-Arnold网络（KANs）在格兰杰因果关系检测中的应用，并将其能力与多层感知机（MLP）进行比较，为神经格兰杰因果关系模型的定义做出了贡献。在这项工作中，我们开发了一个名为格兰杰因果关系KAN（GC-KAN）的框架，以及一个专门为格兰杰因果关系检测设计的定制化训练方法。我们在向量自回归（VAR）模型和混沌Lorenz-96系统上测试了该框架，分析了KAN通过识别格兰杰因果关系来稀疏化输入特征的能力，从而为格兰杰因果关系检测提供了一个简洁而准确的模型。我们的研究结果表明，KAN在识别可解释的格兰杰因果关系方面有潜力超越MLP，尤其是在高维设置下识别稀疏格兰杰因果关系模式的能力，并且更广泛地展示了人工智能在发现物理系统动力学规律因果关系方面的潜力。