@inproceedings{cui-etal-2024-odyssey,
    title = "The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning",
    author = {Cui, Shaobo  and
      Jin, Zhijing  and
      Sch{\"o}lkopf, Bernhard  and
      Faltings, Boi},
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.932/",
    doi = "10.18653/v1/2024.emnlp-main.932",
    pages = "16722--16763",
    abstract = "Understanding commonsense causality is a unique mark of intelligence for humans. It helps people understand the principles of the real world better and benefits the decision-making process related to causation. For instance, commonsense causality is crucial in judging whether a defendant{'}s action causes the plaintiff{'}s loss in determining legal liability. Despite its significance, a systematic exploration of this topic is notably lacking. Our comprehensive survey bridges this gap by focusing on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality, synthesizing insights from over 200 representative articles. Our work aims to provide a systematic overview, update scholars on recent advancements, provide a practical guide for beginners, and highlight promising future research directions in this vital field. A summary of the related literature is available at https://github.com/cui-shaobo/causality-papers ."
}