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arXiv · 1903.02278

Causal Discovery Toolbox: Uncover causal relationships in Python

Diviyan Kalainathan·Olivier Goudet
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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 the 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. 'cdt' is available under the MIT License at https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox.

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Diviyan Kalainathan, Olivier Goudet. 2019-03-06. Causal Discovery Toolbox: Uncover causal relationships in Python. https://arxiv.org/abs/1903.02278

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