Causal Discovery Toolbox: Uncovering Causal Relationships in Python
Abstract
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the `Bnlearn' and `Pcalg' packages, together with algorithms for pairwise causal discovery such as ANM.
Cite
Text
Kalainathan et al. "Causal Discovery Toolbox: Uncovering Causal Relationships in Python." Journal of Machine Learning Research, 2020.Markdown
[Kalainathan et al. "Causal Discovery Toolbox: Uncovering Causal Relationships in Python." Journal of Machine Learning Research, 2020.](https://mlanthology.org/jmlr/2020/kalainathan2020jmlr-causal/)BibTeX
@article{kalainathan2020jmlr-causal,
title = {{Causal Discovery Toolbox: Uncovering Causal Relationships in Python}},
author = {Kalainathan, Diviyan and Goudet, Olivier and Dutta, Ritik},
journal = {Journal of Machine Learning Research},
year = {2020},
pages = {1-5},
volume = {21},
url = {https://mlanthology.org/jmlr/2020/kalainathan2020jmlr-causal/}
}