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.

翻译：因果发现是从数据中识别变量间因果关系的过程。多年来，研究者们基于数据的统计特性提出了多种方法，以揭示潜在的因果机制。本研究首先介绍了因果发现中的常见术语，随后全面讨论了在不同场景下用于识别因果边界的各类方法。我们进一步探讨了用于评估因果发现算法性能的基准数据集、可直接进行因果发现的现有工具，以及评估这些方法的常用指标。最后，总结了因果发现中面临的普遍挑战，并讨论了其在多个领域的应用。