The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning

Shaobo Cui, Zhijing Jin, Bernhard Schölkopf, Boi Faltings

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 .
Anthology ID:
2024.emnlp-main.932
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16722–16763
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.932/
DOI:
10.18653/v1/2024.emnlp-main.932
Bibkey:
Cite (ACL):
Shaobo Cui, Zhijing Jin, Bernhard Schölkopf, and Boi Faltings. 2024. The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16722–16763, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning (Cui et al., EMNLP 2024)
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PDF:
https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.932.pdf
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