CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Wei Zhang, Thomas K. Panum, Somesh Jha, Prasad Chalasani, David Page

Research output: Contribution to journalConference article in JournalResearchpeer-review

18 Downloads (Pure)


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.
Original languageEnglish
Book seriesThe Proceedings of Machine Learning Research
Pages (from-to)11235-11245
Publication statusPublished - Nov 2020
EventInternational Conference on Machine Learning - Virtuel konference
Duration: 13 Jun 202018 Jun 2020


ConferenceInternational Conference on Machine Learning
LocationVirtuel konference


Dive into the research topics of 'CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods'. Together they form a unique fingerprint.

Cite this