Interpretable Models for Granger Causality Using Self-explaining Neural Networks

TL;DR

This paper introduces an interpretable neural network framework for nonlinear Granger causality analysis, enabling sign detection and temporal variability inspection, with competitive performance on simulated data.

Contribution

It extends self-explaining neural networks to infer multivariate Granger causality, providing enhanced interpretability and sign detection capabilities.

Findings

Performs comparably to baseline methods in causality inference.

Achieves superior accuracy in detecting causal effect signs.

Offers a more interpretable alternative to existing neural network approaches.

Abstract

Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate 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, since in addition to relational inference, it also allows detecting signs of Granger-causal effects and inspecting their variability over time. In comprehensive experiments on simulated data, we show that our framework performs on par with several powerful baseline methods at inferring Granger causality and that it achieves better performance at inferring interaction signs. The results suggest that our framework is a viable and more interpretable alternative to sparse-input neural networks for inferring Granger causality.