Chalasani, Prasad

Jha, Somesh

Page, David

Panum, Thomas K.

Zhang, Wei

English

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.<br/

CAUSE:Learning Granger Causality from Event Sequences using Attribution Methods

https://vbn.aau.dk/files/401035040/zhang20v.pdf

CAUSE:Learning Granger Causality from Event Sequences using Attribution Methods

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