  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.
