Causal Discovery Toolbox: Uncover causal relationships in Python

About

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.

Diviyan Kalainathan, Olivier Goudet · 2019

Related benchmarks

Task	Dataset	Result
Causal Discovery	Synthetic DAGs	TPR0.83	125
DAG learning	Synthetic (test)	SID7137	101
Causal Discovery	Synthetic Data	Runtime2829.3	57
Causal Discovery	Synthetic DAG data (test)	TPR89	40
Causal Discovery	Synthetic DAG data	TPR83	40
Causal Discovery	Synthetic DAG Datasets	Runtime (s)3727.4	14
Learning Directed Acyclic Graphs	Synthetic DAGs Gumbel noise distribution 1.0 (test)	SHD350	5
Learning Directed Acyclic Graphs	Synthetic DAGs Default: 100 nodes, 400 edges, ER, Gaussian, n=1000 1.0 (test)	SHD325	5
Learning Directed Acyclic Graphs	Synthetic DAGs High edge density: 1000 edges 1.0 (test)	SHD961	4
Learning Directed Acyclic Graphs	Synthetic DAGs Dense root causes: p=0.5 1.0 (test)	SHD271	4