Granger Causality Detection with Kolmogorov-Arnold Networks

• URL: http://arxiv.org/abs/2412.15373v1
• Date: Thu, 19 Dec 2024 20:10:34 GMT
• Title: Granger Causality Detection with Kolmogorov-Arnold Networks
• Authors: Hongyu Lin, Mohan Ren, Paolo Barucca, Tomaso Aste,
• Abstract summary: This study contributes to the definition of neural Granger causality models. We develop a framework called Granger Causality KAN (GC-KAN) along with a tailored training approach designed specifically for Granger causality detection. Our findings show the potential of KANs to outperform Kolmogorov-Arnoldgressive networks (KANs) in discerning interpretable Granger causal relationships.
• Score: 20.96356350801151
• License:
• Abstract: 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.

Related papers

• On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks [56.78271181959529]
  Kolmogorov--Arnold Networks (KANs) have gained significant attention in the deep learning community. Empirical investigations demonstrate that KANs optimized via gradient descent (SGD) are capable of achieving near-zero training loss.
  arXiv  Detail & Related papers  (2024-10-10T15:34:10Z)
• Jacobian Regularizer-based Neural Granger Causality [45.902407376192656]
  We propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach. Our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer. Our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality.
  arXiv  Detail & Related papers  (2024-05-14T17:13:50Z)
• Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes [24.956802640469554]
  Instance-wise Self-Attentive Hawkes Processes (ISAHP) is a novel deep learning framework that can directly infer the Granger causality at the instance level. ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models.
  arXiv  Detail & Related papers  (2024-02-06T05:46:51Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent [50.4531316289086]
  Gradient Descent (SGD) has been the method of choice for learning large-scale non-root models. An overarching paper is providing general conditions SGD converges, assuming that GF on the population loss converges. We provide a unified analysis for GD/SGD not only for classical settings like convex losses, but also for more complex problems including Retrieval Matrix sq-root.
  arXiv  Detail & Related papers  (2022-10-13T03:55:04Z)
• Deep Recurrent Modelling of Granger Causality with Latent Confounding [0.0]
  We propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. We demonstrate the model performance on non-linear time series for which the latent confounder influences the cause and effect with different time lags.
  arXiv  Detail & Related papers  (2022-02-23T03:26:22Z)
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs [51.33152272781324]
  Graph Neural Networks (GNNs) are proposed without considering the distribution shifts between training and testing graphs. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. We propose a general causal representation framework, called StableGNN, to eliminate the impact of spurious correlations.
  arXiv  Detail & Related papers  (2021-11-20T18:57:18Z)
• Inductive Granger Causal Modeling for Multivariate Time Series [49.29373497269468]
  We propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals.
  arXiv  Detail & Related papers  (2021-02-10T07:48:00Z)
• Interpretable Models for Granger Causality Using Self-explaining Neural Networks [4.56877715768796]
  We propose a novel framework for inferring Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality.
  arXiv  Detail & Related papers  (2021-01-19T12:59:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.