Causal Discovery Toolbox: Uncover causal relationships in Python

By Diviyan Kalainathan, Olivier Goudet. 2019

96 citations recorded by the source corpus.

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 this https URL.

Subfields: Artificial Intelligence, Causal Inference & Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Richard Bowden
2. Necati Cihan Camgöz
3. Matthew James Vowels

In Causal Inference & Discovery

1. Richard Bowden
2. Necati Cihan Camgöz
3. Matthew James Vowels

Citations in the corpus

• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “Method Keywords & Software causaleffect [233] general causality, R daggity [230] general causality, R dosearch [232] causal effect identification, R Causal Discovery Toolbox [121] causal discovery, Python pcalg [123] causal discovery, R bnlearn [211] causal discovery, R rEDM [180] dynamic modeling a”