Causal learner: a toolbox for causal structure and Markov blanket learning
Date
2022
Authors
Ling, Z.
Yu, K.
Zhang, Y.
Liu, L.
Li, J.
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Journal Title
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Journal article
Citation
Pattern Recognition Letters, 2022; 163:92-95
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Abstract
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 abundant 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.
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Data source: Supplementary materials, https://doi.org/10.1016/j.patrec.2022.09.021
Link to a related website: https://unpaywall.org/10.1016/j.patrec.2022.09.021, Open Access via Unpaywall
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Copyright 2022 Elsevier
Access Condition Notes: Accepted manuscript available after 1 October 2024