Causal Discovery Toolbox: Uncovering causal relationships in Python
Diviyan Kalainathan, Olivier Goudet, Ritik Dutta; 21(37):1−5, 2020.
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.
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