Causal Discovery Toolbox: Uncover causal relationships in Python

TL;DR

The paper introduces 'cdt', an open-source Python framework that facilitates causal discovery from observational data, integrating multiple algorithms for causal graph and mechanism modeling.

Contribution

It provides an end-to-end Python toolkit combining existing algorithms for causal discovery, making causal analysis more accessible and comprehensive.

Findings

Includes algorithms from Bnlearn and Pcalg

Supports pairwise causal discovery methods like ANM

Offers an integrated, open-source Python package

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.