Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, it measures effects of treatments in observational data based on experimental designs and rigorous statistical inference to draw causal statements. One of the most influential framework in quantifying causal effects is the potential outcomes framework. On the other hand, causal graphical models utilizes directed edges to represent causalities and encodes conditional independence relationships among variables in the graphs. A series of research has been done both in reading-off conditional independencies from graphs and in re-constructing causal structures. In recent years, the most state-of-art research in causal inference starts unifying the different causal inference frameworks together. This survey aims to provide a review of the past work on causal inference, focusing mainly on potential outcomes framework and causal graphical models. We hope that this survey will help accelerate the understanding of causal inference in different domains.

翻译：因果关系推断是一种具有多学科进化和应用的科学,一方面,它衡量基于实验设计和严格统计推论的观察数据的处理效果,以得出因果关系说明;在量化因果关系方面最有影响力的框架之一是潜在结果框架;另一方面,因果图形模型利用定向边缘代表因果关系,并编码图中变量之间的有条件独立关系;在从图表中读取有条件的有条件依赖性和重新构建因果结构方面进行了一系列研究;近年来,最先进的因果推断研究开始将不同的因果推断框架统一起来;这项调查旨在审查过去关于因果推断的工作,主要侧重于潜在结果框架和因果图形模型;我们希望这项调查将有助于加速理解不同领域的因果推断。