The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continued advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models, and its core contributions are as follows: 1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; 2) we outline some typical applications of causal effect estimation to industry; 3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

翻译：因果关系在人类认知领域中占据着至关重要的地位。过去数十年间，因果效应估计在计算机科学、医学、经济学及工业应用等多个学科领域取得了显著进展。随着深度学习方法的持续发展，利用反事实数据估计因果效应的相关应用呈现显著增长趋势。深度因果模型通常将协变量特征映射至表征空间，进而设计各类目标函数以实现反事实数据的无偏估计。与现有机机器学习因果模型综述不同，本综述主要聚焦于深度因果模型的概述，其核心贡献包括：1）从发展时间线和研究方法分类双重视角提供深度因果模型的全面综述；2）概述因果效应估计在工业领域的典型应用；3）系统梳理并分类分析相关数据集、源代码及实验成果。