COLA: Contextualized Commonsense Causal Reasoning from the Causal
  Inference Perspective
Zhaowei Wang Quyet V. Do Hongming Zhang Jiayao Zhang Weiqi Wang Tianqing Fang Yangqiu Song Ginny Y. Wong Simon See
Detecting commonsense causal relations (causation) between events has long
been an essential yet challenging task. Given that events are complicated, an
event may have different causes under various contexts. Thus, exploiting
context plays an essential role in detecting causal relations. Meanwhile,
previous works about commonsense causation only consider two events and ignore
their context, simplifying the task formulation. This paper proposes a new task
to detect commonsense causation between two events in an event sequence (i.e.,
context), called contextualized commonsense causal reasoning. We also design a
zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to
solve the task from the causal inference perspective. This framework obtains
rich incidental supervision from temporality and balances covariates from
multiple timestamps to remove confounding effects. Our extensive experiments
show that COLA can detect commonsense causality more accurately than baselines.