  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.
