Causal Discovery (CD) is the process of identifying the cause-effect
relationships among the variables from data. Over the years, several methods
have been developed primarily based on the statistical properties of data to
uncover the underlying causal mechanism. In this study we introduce the common
terminologies in causal discovery, and provide a comprehensive discussion of
the approaches designed to identify the causal edges in different settings. We
further discuss some of the benchmark datasets available for evaluating the
performance of the causal discovery algorithms, available tools to perform
causal discovery readily, and the common metrics used to evaluate these
methods. Finally, we conclude by presenting the common challenges involved in
CD and also, discuss the applications of CD in multiple areas of interest.

本研究介绍了因果发现中常用的术语，讨论了用于不同场景中识别因果边的方法，还探讨了用于评估因果发现算法性能的基准数据集、现有工具和常用指标。最后，我们总结了因果发现中存在的常见挑战，并讨论了因果发现在多个领域的应用。

针对时态和非时态数据的因果发现方法调查

A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data

Automatic extraction of cause-effect relationships from natural language
texts is a challenging open problem in Artificial Intelligence. Most of the
early attempts at its solution used manually constructed linguistic and
syntactic rules on small and domain-specific data sets. However, with the
advent of big data, the availability of affordable computing power and the
recent popularization of machine learning, the paradigm to tackle this problem
has slowly shifted. Machines are now expected to learn generic causal
extraction rules from labelled data with minimal supervision, in a domain
independent-manner. In this paper, we provide a comprehensive survey of causal
relation extraction techniques from both paradigms, and analyse their relative
strengths and weaknesses, with recommendations for future work.

本文综述了早期人类构造的语言和句法规则以及最新机器学习技术在提取因果关系上的应用，并分析了它们的优劣，建议未来的研究方向。