Methods and tools for causal discovery and causal inference
Methods and tools for causal discovery and causal inference
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DOI：
10.1002/widm.1449
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发表时间：
2022-01-19
影响因子：
7.8
通讯作者：
Gama, Joao
中科院分区：
文献类型：
--
作者：
Nogueira, Ana Rita;Pugnana, Andrea;Gama, Joao
因果关系是一个复杂的概念，它的发展植根于几个领域，如统计学，经济学，流行病学，计算机科学和哲学。近年来，因果关系的研究已经成为人工智能社区的重要组成部分，因为因果关系可以成为克服基于相关性的机器学习系统的一些局限性的关键工具。因果关系研究一般可分为两个主要分支，即因果发现和因果推理。前者侧重于直接从观测数据中获取因果知识。后者旨在估计某一变量的变化对感兴趣的结果的影响。本文旨在介绍为这两项任务开发的几种方法。这一调查不仅侧重于理论方面。而且还为感兴趣的研究人员和从业人员提供了一个实用的工具包，包括软件，数据集和运行示例。这篇文章分类下：数据开发>因果发现数据和知识的基本概念>可解释的人工智能技术>机器学习
Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning