@inproceedings{wang-etal-2023-cola,
    title = "{COLA}: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective",
    author = "Wang, Zhaowei  and
      Do, Quyet V.  and
      Zhang, Hongming  and
      Zhang, Jiayao  and
      Wang, Weiqi  and
      Fang, Tianqing  and
      Song, Yangqiu  and
      Wong, Ginny  and
      See, Simon",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.288",
    doi = "10.18653/v1/2023.acl-long.288",
    pages = "5253--5271",
    abstract = "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.",
}

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.
Wang, Zhaowei Do, Quyet V. Zhang, Hongming Zhang, Jiayao Wang, Weiqi Fang, Tianqing Song, Yangqiu Wong, Ginny See, Simon
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective