因果学习器：用于因果结构和马尔可夫毯学习的工具箱
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
AI总结：
因果学习器是一个开源工具箱，集成了数据生成、全局和局部因果结构学习、马尔可夫毯学习及评估功能，支持新算法的开发与评估。
摘要：
因果学习器是一个用于从数据中学习因果结构和马尔可夫毯（MB）的工具箱。它集成了用于生成模拟贝叶斯网络数据的函数、一组最先进的全局因果结构学习算法、一组最先进的局部因果结构学习算法、一组最先进的MB学习算法以及用于评估算法的函数。因果学习器的数据生成部分用R编写，其余部分用MATLAB编写。因果学习器旨在为研究人员和实践者提供一个用于从数据中进行因果学习以及开发和评估新因果学习算法的开源平台。因果学习器项目可在http://bigdata.ahu.edu.cn/causal-learner获取。
英文摘要：
Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of-the-art local causal structure learning algorithms, a set of state-of-the-art MB learning algorithms, and functions for evaluating algorithms. The data generation part of Causal Learner is written in R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to provide researchers and practitioners with an open-source platform for causal learning from data and for the development and evaluation of new causal learning algorithms. The Causal Learner project is available at http://bigdata.ahu.edu.cn/causal-learner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
