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 language | English |
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Book series | The Proceedings of Machine Learning Research |
Volume | 119 |
Pages (from-to) | 11235-11245 |
ISSN | 2640-3498 |
Publication status | Published - Nov 2020 |
Event | International Conference on Machine Learning - Virtuel konference Duration: 13 Jun 2020 → 18 Jun 2020 |
Conference
Conference | International Conference on Machine Learning |
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Location | Virtuel konference |
Period | 13/06/2020 → 18/06/2020 |