因果学习方法和应用概述

Overview of Causal Learning Techniques and Applications

机器学习是人工智能和数据科学的核心所在,被广泛应用于教育、交通运输和制造业等领域;随着机器学习的发展及应用领域的延伸,模型在可解释性和公平性等方面显现了一些需要解决的问题。因果学习作为一种将因果关系和机器学习技术相结合的方法,可以增强模型的可解释性,解决公平性等问题,其研究已逐渐成为学术界的热点。因此,在介绍因果学习的相关理论知识的基础上,根据因果学习所能解决的问题对因果解释、因果监督学习、因果公平、因果强化学习等技术进行了全方位的分析概述;从多角度归纳了因果学习在医学、农业和智能制造等领域的应用。最后,总结了因果学习存在的一些开放性问题和挑战,并给出了未来的研究方向,旨在推动因果学习的不断发展。

Machine learning is the core of artificial intelligence and data science,and is widely used in education,trans-portation and manufacturing.With the development of machine learning and the extension of application fields,the models have revealed some problems to be solved in terms of interpretability and fairness.Causal learning(CL),as a method com-bining causality and machine learning techniques,can enhance the interpretability of the model and solve the problems of fairness,and its research has gradually become a hot spot in the academic world.Therefore,based on the introduction of the relevant theoretical knowledge of CL,the techniques of causal explanation,causal supervised learning,causal fairness,and causal reinforcement learning are firstly analyzed and outlined in an all-round way according to the problems that can be solved by CL.Secondly,the applications of CL in the fields of medicine,agriculture and intelligent manufacturing are summarized from multiple perspectives.Finally,some open problems and challenges of CL are summarized,and future research directions are given,aiming to promote the continuous development of CL.