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Article Dans Une Revue Journal of Machine Learning Research Année : 2020

Causal Discovery Toolbox: Uncovering causal relationships in Python

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Résumé

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' (Scutari, 2018) and 'Pcalg' (Kalisch et al., 2018) packages, together with algorithms for pairwise causal discovery such as ANM (Hoyer et al., 2009). Cdt is available under the MIT License at https://github.com/FenTechSolutions/CausalDiscoveryToolbox.
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Dates et versions

hal-02945539 , version 1 (22-09-2020)

Licence

Paternité - CC BY 4.0

Identifiants

  • HAL Id : hal-02945539 , version 1

Citer

Diviyan Kalainathan, Olivier Goudet, Ritik Dutta. Causal Discovery Toolbox: Uncovering causal relationships in Python. Journal of Machine Learning Research, 2020, 21, pp.1-5. ⟨hal-02945539⟩
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