A Survey of Deep Causal Models and Their Industrial Applications

The concept of causality plays a significant role in human cognition. In the past few decades, causal effect estimation has been well developed in many fields, such as computer science, medicine, economics, and other industrial applications. With the advancement of deep learning, it has been increasingly applied in causal effect estimation against 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 paper mainly focuses on the overview of the deep causal models, and its core contributions are as follows: 1) we summarize the popularly adopted relevant metrics under multiple treatment, continuous-dose treatment and times series treatment; 2) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; 3) we outline some typical applications of causal effect estimation to industry; 4) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.