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.

翻译：摘要：检测事件间的常识性因果关系（因果性）长期以来一直是一项重要但具有挑战性的任务。由于事件具有复杂性，同一事件在不同语境下可能有不同的原因。因此，利用语境在因果关系的检测中起着关键作用。与此同时，以往关于常识因果性的研究仅考虑两个孤立事件，忽略其语境，从而简化了任务设定。本文提出了一项新任务：检测事件序列（即语境）中两个事件间的常识性因果关系，称为上下文化常识因果推理。我们还设计了一个零样本框架：COLA（上下文化常识因果推理器），从因果推断视角解决该任务。该框架从时序性中获取丰富的间接监督信号，并通过平衡多个时间戳的协变量来消除混杂效应。大量实验表明，COLA在检测常识性因果关系方面比基线方法更准确。