  In this paper, we describe our shared task submissions for Subtask 2 in
CASE-2022, Event Causality Identification with Casual News Corpus. The
challenge focused on the automatic detection of all cause-effect-signal spans
present in the sentence from news-media. We detect cause-effect-signal spans in
a sentence using T5 -- a pre-trained autoregressive language model. We
iteratively identify all cause-effect-signal span triplets, always conditioning
the prediction of the next triplet on the previously predicted ones. To predict
the triplet itself, we consider different causal relationships such as
cause$\rightarrow$effect$\rightarrow$signal. Each triplet component is
generated via a language model conditioned on the sentence, the previous parts
of the current triplet, and previously predicted triplets. Despite training on
an extremely small dataset of 160 samples, our approach achieved competitive
performance, being placed second in the competition. Furthermore, we show that
assuming either cause$\rightarrow$effect or effect$\rightarrow$cause order
achieves similar results.
