CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

• URL: http://arxiv.org/abs/2002.07906v1
• Date: Tue, 18 Feb 2020 22:21:11 GMT
• Title: CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
• Authors: Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, and David Page
• Abstract summary: We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. We propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. We demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.
• Score: 25.04848774593105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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