Hybrid Improved Physarum Learner for Structure Causal Discovery

Joao Soares, Vitor Barth, Alan Eckeli, Carlos Maciel

2023

Abstract

Causal discovery is the problem of estimating a joint distribution from observational data. In recent years, hybrid algorithms have been proposed to overcome computational problems that lead to better results. This work presents a hybrid approach that combines PC algorithm independence tests with a bio-inspired Improved Physarum Learner algorithm. The combination indicates improvement in computational time spent and yet consistent structural results.

Paper Citation

in Harvard Style

Soares J., Barth V., Eckeli A. and Maciel C. (2023). Hybrid Improved Physarum Learner for Structure Causal Discovery. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 234-240. DOI: 10.5220/0011671000003414

in Bibtex Style

@conference{biosignals23,
author={Joao Soares and Vitor Barth and Alan Eckeli and Carlos Maciel},
title={Hybrid Improved Physarum Learner for Structure Causal Discovery},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={234-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011671000003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Hybrid Improved Physarum Learner for Structure Causal Discovery
SN - 978-989-758-631-6
AU - Soares J.
AU - Barth V.
AU - Eckeli A.
AU - Maciel C.
PY - 2023
SP - 234
EP - 240
DO - 10.5220/0011671000003414
PB - SciTePress