Causal Discovery Toolbox: Uncover causal relationships in Python
This provides a practical tool for researchers and practitioners in fields like data science and machine learning to model causal mechanisms, though it is incremental as it integrates existing algorithms.
The paper introduces an open-source Python framework for causal discovery from observational data, enabling the recovery of causal graph structures and relationships between variables.
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.