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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods.

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.

SUBMITTER: Zhang W

PROVIDER: S-EPMC7710164 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods.

Zhang Wei W Panum Thomas Kobber TK Jha Somesh S Chalasani Prasad P Page David D

Proceedings of machine learning research 20200701

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 C ...[more]

PMID: 33274352

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