Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs is recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.

翻译：揭示支配现象的内在规律是科学进步的核心。当目标是以因果方式建模不同维度间的相互作用时尤为如此。事实上，因果推断本身就是专门为量化连接因果关系的底层关联而设计的。因果发现是更广泛的因果关系领域的一个分支，旨在从数据中（在可行情况下）恢复因果图，从而实现对因果效应的识别与估计。本文以统一视角探讨最新研究进展，系统综述不同设定下开发的现有算法，报告实用工具与数据集，并通过真实世界应用案例阐释这些方法为何及如何能被有效利用。