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.
翻译:因果关系概念在人类认知中起着重要作用。在过去几十年中,在计算机科学、医学、经济学和其他工业应用等许多领域,因果关系估计已经发展得相当好。随着深层次学习的进展,这种估计越来越多地用于对反事实数据的因果关系估计。典型的情况是,深层次的因果关系模型将共变体的特征映射成一个代表空间,然后设计各种客观功能,以便不偏袒地估计反事实数据。与关于机器学习中因果关系模型的现有调查不同,本文主要侧重于深刻因果关系模型的概览,其核心贡献如下:1)我们总结了在多种治疗、连续剂量治疗和时间序列治疗下普遍采用的相关指标;2)我们从发展时间表和方法分类角度对深刻因果关系模型的全面概述;3)我们概述了对工业的因果关系估计的一些典型应用;4)我们还努力对有关数据集、源代码和实验进行详细分类和分析。