The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfaced as a focal research area. Nonetheless, the broadening of original causal concepts and theories to such complex, non-statistical data has been met with serious challenges. In response, our study proposes redefinitions of causal data into three distinct categories from the standpoint of causal structure and representation: definite data, semi-definite data, and indefinite data. Definite data chiefly pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning, including time-series, images, text, and others. Indefinite data is an emergent research sphere inferred from the progression of data forms by us. To comprehensively present these three data paradigms, we elaborate on their formal definitions, differences manifested in datasets, resolution pathways, and development of research. We summarize key tasks and achievements pertaining to definite and semi-definite data from myriad research undertakings, present a roadmap for indefinite data, beginning with its current research conundrums. Lastly, we classify and scrutinize the key datasets presently utilized within these three paradigms.

翻译：因果模型与深度学习的融合，能够处理日益复杂的数据集（如图像内的因果关联或文本组件间的因果关系），已成为一个备受关注的研究领域。然而，将原始的因果概念与理论拓展至此类复杂的非统计数据，面临着严峻挑战。为此，我们从因果结构与表示的角度出发，重新将因果数据定义为三种类别：确定性数据、半确定性数据与不确定性数据。确定性数据主要涵盖传统因果场景中使用的统计数据，而半确定性数据则指代与深度学习相关的一系列数据格式，包括时间序列、图像、文本及其他类型。不确定性数据则是我们根据数据形式的演化推断出的新兴研究领域。为全面阐述这三种数据范式，我们详细介绍了其形式定义、数据集差异、解决途径及研究进展。我们总结了众多研究在确定性及半确定性数据方面取得的关键任务与成果，并针对不确定性数据，从当前研究难题出发提出了发展路线图。最后，我们对这三种范式中目前使用的关键数据集进行了分类与细究。