The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning (2024)

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 .

## introduction we do not have knowledge of a thing until we have grasped its why, that is to say, its cause. -aristotle, 384-322 bc causality @xcite @xcite @xcite has been a cornerstone concept spanning both scientific and philosophical spheres since aristotle's era @xcite . in this paper, we focus particularly on commonsense causality, which we formally define as the general public's intuition about how the occurrence of one event, fact, process, state, or object (the cause) plays a role in bringing about or contributing to the happening of another (the effect). for example, we know that a rainy morning precipitates traffic congestion or that eating too much leads to weight gain. this innate comprehension of causeand-effect dynamics is frequently termed "commonsense causality". it has applications across fields such as medical diagnosis @xcite , psychology @xcite , behavioral science @xcite , economics @xcite , and legal systems @xcite (see more applications in app. a). despite its significance, the field still lacks a comprehensive overview of commonsense causality foot_0 . while there are several survey papers on causal inference @xcite @xcite and commonsense knowledge @xcite , a comprehensive overview of the intersection of these two domains -commonsense causalityremains missing. the importance of this gap has been further highlighted by recent advancements in large language models (llms) @xcite , which underscore commonsense causality as a pivotal reasoning capability for models. this emerging focus accentuates the urgent need for an in-depth overview. to fill this blank, we conduct an extensive and up-to-date survey of commonsense causality, with comprehensive coverage of its taxonomy, benchmarks, acquisition methods, as well as qualitative and quantitative commonsense causality taxonomy of causality ( §2) first-principle causality ( § 2.2) benchmarks ade @xcite , @xcite , ihdp @xcite , @xcite , empirical causality text format benchmarks word: semeval10-t8 @xcite , semeval20-t5 @xcite . clause: temporal-causal @xcite , @xcite , biocause @xcite , @xcite , tcr @xcite , pdtb @xcite , causalbank @xcite , semeval20-t5 @xcite , scite @xcite ) sentence: copa @xcite , causaltimebank @xcite , caters @xcite , @xcite , esl @xcite , timetravel @xcite , xcopa @xcite ), e-care @xcite , cosim @xcite , crass @xcite , δ-causal @xcite , copes @xcite , ifqa @xcite (ii) temporal constraint: ccm @xcite ; caters @xcite ; (iii) logic rules: @xcite @xcite . quantitative measurement( § 4.2) word co-occurrence: wordcs @xcite , ceq @xcite , cesar @xcite relation words: rock @xcite , cola @xcite figure 2 : taxonomy of commonsense causality in various aspects. the benchmarks, datasets, and methods in blue color are about counterfactual. leaf nodes with different colors are associated with different sections of this survey. we start by presenting a taxonomy of commonsense causality based on different types of commonsense knowledge (e.g., physical, social, biological, and temporal commonsense) and different levels of uncertainty ( § 2). leveraging this taxonomy, we methodically categorize 37 existing benchmarks to provide a structured overview. following this, we discuss three main approaches to acquiring benchmarks conducive to commonsense causality research: extractive ( § 3.1), generative ( § 3.2), and manual annotation methods ( § 3.3). beyond introducing each approach, we also systematically compare the merits and demerits of these three approaches, providing insights for future work on commonsense causality acquisition. furthermore, we classify the existing causality reasoning methods into two categories based on their way of managing the intrinsic uncertainty within commonsense causality. the first type is qualitative approaches ( § 4.1), which simplify causal reasoning as a classification task and bypass the uncertainty. the second type is quantitative approaches ( § 4.2), which employ metrics to measure causal strength, thereby quantifying the uncertainty. this classification not only aids in understanding the diverse methodologies but also highlights the varied strategies employed to tackle uncertainty in commonsense causality reasoning. lastly, we suggest several promising directions in the field of commonsense causality in § 5. these topics include the exploration of contextual nuances, the analysis of complex structures, the measurement of probabilistic causality, the understanding of temporal dynamics, and the integration of multimodal data. this exploration aims to offer a roadmap for future research. the contributions of our survey are threefold: our review focused on articles related to commonsense causality from leading peer-reviewed venues in nlp and ai research, such as acl, emnlp, naacl, aaai, neurips, iclr, icml, and ijcai. we utilized a keyword-based selection strategy, prioritizing papers featuring terms like "causality", "acquisition", "causal reasoning", and "commonsense" in their titles or abstracts. additionally, we explored github repositories related to causal nlp papers to complement our search. there are also some papers from the philosophy community that help illustrate the concepts related to causality. the scope of this survey. determining the precise end line of the scope for this survey presents a significant challenge: the domain of commonsense reasoning encompasses a vast area, within which causality plays a crucial role across a substantial 2 due to the page limit, we present a main overview of commonsense causality research in the main text. we also provide extensive supplementary information in apps a to l, covering applications, preliminary knowledge, related survey works, other taxonomies, details of uncertainty, acquisition methods, and benchmarks, concepts of causality, nlp techniques, linguistic causality, causal inference, and handbook for beginners. ## causality acquisition common methods for acquiring commonsense causality benchmarks are categorized into three main approaches: extractive methods ( § 3.1), generative methods ( § 3.2), and manual annotation methods ( § 3.3). these methods are summarized in figure 2 with a hidden orange background color ## reasoning over causality this section reviews qualitative and quantitative causal reasoning approaches for addressing uncertainty in commonsense causality, as discussed in § 2.2. qualitative methods ( § 4.1) treat causal reasoning as a 0/1 classification task, while quantitative methods ( § 4.2) quantify causality strength numerically. this section relates to the content highlighted in pale blue color in figure 2 . a detailed chronological overview of these advancements and their impact on causal reasoning is provided in figure 8 and app. h. ## conclusion in this survey, we present an overview of commonsense causality, including its taxonomy, benchmarks, and data acquisition methods, along with qualitative and quantitative reasoning approaches. furthermore, we shed light on several future promising research directions. our work, drawing on insights from over 200 articles, aims to provide a thorough understanding of commonsense causality in the era of llms. additionally, we include a pragmatic handbook in app. l for researchers interested in further exploration of this field.