Granger causality detection with Kolmogorov-Arnold networks
Hongyu Lin; Mohan Ren; Paolo Barucca; Tomaso Aste · 2026 · Physical Review Research
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Identifying causal relationships between variables is fundamental to scientific inquiry, and the Granger causality (GC) framework is widely used to provide indications of causal relationships in time series analysis. While the original GC approach was limited to linear interactions, it was later extended to nonlinear cases using neural networks, particularly multilayer perceptrons (MLPs). In this work, we introduce—for the first time—the use of Kolmogorov-Arnold Networks (KANs) for detecting nonlinear Granger causality (GC-KAN), leveraging their functional decomposition properties for enhanced
Abstract by Hongyu Lin; Mohan Ren; Paolo Barucca; Tomaso Aste, Physical Review Research (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1103/y1tc-4ms4