A Review and Roadmap of Deep Causal Model from Different Causal
  Structures and Representations
Hang Chen Keqing Du Chenguang Li Xinyu Yang
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.