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

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Abstract

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
Volume119
Pages (from-to)11235-11245
ISSN2640-3498
Publication statusPublished - Nov 2020
EventInternational Conference on Machine Learning - Virtuel konference
Duration: 13 Jun 202018 Jun 2020

Conference

ConferenceInternational Conference on Machine Learning
LocationVirtuel konference
Period13/06/202018/06/2020

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