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

A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations