Learning Conditional Granger Causal Temporal Networks

Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian

Abstract: Granger-causality derived from observational time series data is used in many real-world applications where timely interventions are infeasible. However, discovering Granger-causal links in large temporal networks with a large number of nodes and time-lags can lead to \emph{millions} of time-lagged model parameters, which requires us to make sparsity and overlap assumptions. In this paper, we propose to learn time-lagged model parameters with the objective of improving recall of links, while learning to defer predictions when the overlap assumption is violated over observed time series. By learning such conditional time-lagged models, we demonstrate a 25\% increase in the area under the precision-recall curve for discovering Granger-causal links combined with a 18-25\% improvement in forecasting accuracy across three popular and diverse datasets from different disciplines (DREAM3 gene expression, MoCAP human motion recognition and New York Times news-based stock price prediction) with correspondingly large temporal networks, over several baseline models including Multivariate Autoregression, Neural Granger Causality, Graph Neural Networks and Graph Attention models. The observed improvement in Granger-causal link discovery is significant and can potentially further improve prediction accuracy and modeling efficiency in downstream real-world applications leveraging these popular datasets.