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.
|Book series||The Proceedings of Machine Learning Research|
|Publication status||Published - Nov 2020|
|Event||International Conference on Machine Learning - Virtuel konference|
Duration: 13 Jun 2020 → 18 Jun 2020
|Conference||International Conference on Machine Learning|
|Period||13/06/2020 → 18/06/2020|