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.

将因果模型与深度学习相结合，引入了越来越复杂的数据集，如图像内部的因果关联或文本组件之间的因果关联，成为一个研究重点。本研究根据因果结构和表示的观点，对因果数据进行三个不同类别的重新定义：确定数据、半确定数据和不确定数据。通过详细阐述这三个数据范例的正式定义、数据集中所体现的差异、解决途径和研究发展，我们总结了与确定数据和半确定数据相关的关键任务和成就，并提出了关于不确定数据的路线图，以解决当前的研究难题。最后，我们对目前在这三个范例中使用的关键数据集进行了分类和审查。