CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
This addresses the challenge of uncovering causal relationships in event data for fields like healthcare or finance, though it appears incremental by combining existing techniques.
The paper tackles the problem of learning Granger causality from asynchronous event sequences, proposing CAUSE, a framework that uses neural point processes and attribution methods to achieve superior performance in inferring causality across diverse datasets.
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.