CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

• Published in: arXiv.org 2020-02
• Main Authors: Zhang, Wei, Panum, Thomas Kobber, Jha, Somesh, Chalasani, Prasad, Page, David
• Format: Article
• Language: English
• Subjects: Causality; Cointegration analysis; Learning
• ISSN: 2331-8422

We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency. To address these weaknesses, we propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic attribution method. Across multiple datasets riddled with diverse event interdependency, we demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.