  Event Causality Identification (ECI) aims to detect whether there exists a
causal relation between two events in a document. Existing studies adopt a kind
of identifying after learning paradigm, where events' representations are first
learned and then used for the identification. Furthermore, they mainly focus on
the causality existence, but ignoring causal direction. In this paper, we take
care of the causal direction and propose a new identifying while learning mode
for the ECI task. We argue that a few causal relations can be easily identified
with high confidence, and the directionality and structure of these identified
causalities can be utilized to update events' representations for boosting next
round of causality identification. To this end, this paper designs an
*iterative learning and identifying framework*: In each iteration, we construct
an event causality graph, on which events' causal structure representations are
updated for boosting causal identification. Experiments on two public datasets
show that our approach outperforms the state-of-the-art algorithms in both
evaluations for causality existence identification and direction
identification.
