  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.
