Gradient Regularization-based Neural Granger Causality

• URL: http://arxiv.org/abs/2507.11178v1
• Date: Tue, 15 Jul 2025 10:35:29 GMT
• Title: Gradient Regularization-based Neural Granger Causality
• Authors: Meiliang Liu, Huiwen Dong, Xiaoxiao Yang, Yunfang Xu, Zijin Li, Zhengye Si, Xinyue Yang, Zhiwen Zhao,
• Abstract summary: We propose Gradient Regularization-based Neural Granger Causality (GRNGC)<n> GRNGC requires only one time series prediction model and applies $L_1$ regularization to the gradient between model's input and output to infer Granger causality.<n> Numerical simulations on DREAM, Lorenz-96, fMRI, and CausalTime show that GRNGC outperforms existing baselines and significantly reduces computational overhead.
• Score: 1.7365653221505928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advancement of deep learning technologies, various neural network-based Granger causality models have been proposed. Although these models have demonstrated notable improvements, several limitations remain. Most existing approaches adopt the component-wise architecture, necessitating the construction of a separate model for each time series, which results in substantial computational costs. In addition, imposing the sparsity-inducing penalty on the first-layer weights of the neural network to extract causal relationships weakens the model's ability to capture complex interactions. To address these limitations, we propose Gradient Regularization-based Neural Granger Causality (GRNGC), which requires only one time series prediction model and applies $L_{1}$ regularization to the gradient between model's input and output to infer Granger causality. Moreover, GRNGC is not tied to a specific time series forecasting model and can be implemented with diverse architectures such as KAN, MLP, and LSTM, offering enhanced flexibility. Numerical simulations on DREAM, Lorenz-96, fMRI BOLD, and CausalTime show that GRNGC outperforms existing baselines and significantly reduces computational overhead. Meanwhile, experiments on real-world DNA, Yeast, HeLa, and bladder urothelial carcinoma datasets further validate the model's effectiveness in reconstructing gene regulatory networks.

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