  The Event Causality Identification Shared Task of CASE 2022 involved two
subtasks working on the Causal News Corpus. Subtask 1 required participants to
predict if a sentence contains a causal relation or not. This is a supervised
binary classification task. Subtask 2 required participants to identify the
Cause, Effect and Signal spans per causal sentence. This could be seen as a
supervised sequence labeling task. For both subtasks, participants uploaded
their predictions for a held-out test set, and ranking was done based on binary
F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes
the work of the 17 teams that submitted their results to our competition and 12
system description papers that were received. The best F1 scores achieved for
Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing
approaches involved pre-trained language models fine-tuned to the targeted
task. We further discuss these approaches and analyze errors across
participants' systems in this paper.
