Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
This is an incremental contribution that offers a software tool to facilitate causal learning and algorithm development in the field.
The authors introduced Causal Learner, a toolbox that integrates functions for generating simulated Bayesian network data and state-of-the-art algorithms for learning causal structure and Markov blankets, providing an open-source platform for researchers and practitioners.
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.