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CAUSE: Learning granger causality from event sequences using attribution methods

Publication ,  Conference
Zhang, W; Panum, TK; Jha, S; Chalasani, P; Page, D
Published in: 37th International Conference on Machine Learning, ICML 2020
January 1, 2020

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.

Duke Scholars

Published In

37th International Conference on Machine Learning, ICML 2020

Publication Date

January 1, 2020

Volume

PartF168147-15

Start / End Page

11171 / 11181
 

Citation

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Zhang, W., Panum, T. K., Jha, S., Chalasani, P., & Page, D. (2020). CAUSE: Learning granger causality from event sequences using attribution methods. In 37th International Conference on Machine Learning, ICML 2020 (Vol. PartF168147-15, pp. 11171–11181).

Published In

37th International Conference on Machine Learning, ICML 2020

Publication Date

January 1, 2020

Volume

PartF168147-15

Start / End Page

11171 / 11181