Understanding causality helps to structure interventions to achieve specific
goals and enables predictions under interventions. With the growing importance
of learning causal relationships, causal discovery tasks have transitioned from
using traditional methods to infer potential causal structures from
observational data to the field of pattern recognition involved in deep
learning. The rapid accumulation of massive data promotes the emergence of
causal search methods with brilliant scalability. Existing summaries of causal
discovery methods mainly focus on traditional methods based on constraints,
scores and FCMs, there is a lack of perfect sorting and elaboration for deep
learning-based methods, also lacking some considers and exploration of causal
discovery methods from the perspective of variable paradigms. Therefore, we
divide the possible causal discovery tasks into three types according to the
variable paradigm and give the definitions of the three tasks respectively,
define and instantiate the relevant datasets for each task and the final causal
model constructed at the same time, then reviews the main existing causal
discovery methods for different tasks. Finally, we propose some roadmaps from
different perspectives for the current research gaps in the field of causal
discovery and point out future research directions.

本文介绍了基于深度学习的因果关系发现方法，从变量范式的角度将可能的因果发现任务分为三类，并针对每种任务定义并实例化相关数据集和最终的因果模型，并回顾了各种任务的现有因果发现方法。最后，提出了一些研究路线图以解决现有领域的研究空白，并指出未来的研究方向。