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 pragmatic guide for beginners, and highlight promising
future research directions in this vital field.

通过综合 200 篇代表性文献的见解，我们的综合调查桥接了对常识因果推理的认知差距，并在共识因果关系的分类、基准、获取方法、定性推理和定量测量方面提供了系统的概述、最新研究进展、新手们的实用指南以及未来研究方向的重要提示。

常识因果关系的奥德赛：从基础指标到前沿推理

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

Fully supervised models are predominant in Bayesian active learning. We argue
that their neglect of the information present in unlabelled data harms not just
predictive performance but also decisions about what data to acquire. Our
proposed solution is a simple framework for semi-supervised Bayesian active
learning. We find it produces better-performing models than either conventional
Bayesian active learning or semi-supervised learning with randomly acquired
data. It is also easier to scale up than the conventional approach. As well as
supporting a shift towards semi-supervised models, our findings highlight the
importance of studying models and acquisition methods in conjunction.

全监督模型在贝叶斯主动学习中占主导地位，我们认为它们对未标记数据中的信息的忽视不仅损害了预测性能，也影响了关于获取哪些数据的决策。我们提出了一个简单的半监督贝叶斯主动学习框架，发现它比传统的贝叶斯主动学习或随机获取数据的半监督学习能够生成更好的模型。该框架也更易于扩展。除了支持向半监督模型的转变外，我们的发现还强调了研究模型和获取方法的重要性。