Causal Discovery Toolbox Documentation

Package for causal inference in graphs and in the pairwise settings for Python>=3.5. Tools for graph structure recovery and dependencies are included. The package is based on Numpy, Scikit-learn, Pytorch and R.

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It implements lots of algorithms for graph structure recovery (including algorithms from the bnlearn, pcalg packages), mainly based out of observational data.

Install it using pip: (See more details on installation below)

pip install cdt

Open-source project

The package is open-source and under the MIT license, the source code is available at : https://github.com/FenTechSolutions/CausalDiscoveryToolbox

When using this package, please cite: Kalainathan, D., & Goudet, O. (2019). Causal Discovery Toolbox: Uncover causal relationships in Python. arXiv:1903.02278.

Docker images

Docker images are available, including all the dependencies, and enabled functionalities:




Python 3.6 - CPU



Python 3.7 - CPU



Python 3.6 - GPU




The packages requires a python version >=3.5, as well as some libraries listed in the requirements file. For some additional functionalities, more libraries are needed for these extra functions and options to become available. Here is a quick install guide of the package, starting off with the minimal install up to the full installation.


A (mini/ana)conda framework would help installing all those packages and therefore could be recommended for non-expert users.


As some of the key algorithms in the _cdt_ package use the PyTorch package, it is required to install it. Check out their website to install the PyTorch version suited to your hardware configuration: https://pytorch.org

Install the CausalDiscoveryToolbox package

The package is available on PyPi:

pip install cdt

Or you can also install it from source.

$ git clone https://github.com/FenTechSolutions/CausalDiscoveryToolbox.git  # Download the package
$ cd CausalDiscoveryToolbox
$ pip install -r requirements.txt  # Install the requirements
$ python setup.py install develop --user

The package is then up and running ! You can run most of the algorithms in the CausalDiscoveryToolbox, you might get warnings: some additional features are not available

From now on, you can import the library using :

import cdt

Additional : R and R libraries

In order to have access to additional algorithms from various R packages such as bnlearn, kpcalg, pcalg, … while using the _cdt_ framework, it is required to install R.

Check out how to install all R dependencies in the before-install section of the [travis.yml](https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/.travis.yml) file for debian based distributions. The r-requirements file notes all the R packages used by the toolbox.


The following figure shows how the package and its algorithms are structured:

cdt package
|- independence
|  |- graph (Infering the skeleton from data)
|  |  |- Lasso variants (Randomized Lasso[1], Glasso[2], HSICLasso[3])
|  |  |- FSGNN (CGNN[12] variant for feature selection)
|  |  |- Skeleton recovery using feature selection algorithms (RFECV[5], LinearSVR[6], RRelief[7], ARD[8,9], DecisionTree)
|  |
|  |- stats (pairwise methods for dependency)
|     |- Correlation (Pearson, Spearman, KendallTau)
|     |- Kernel based (NormalizedHSIC[10])
|     |- Mutual information based (MIRegression, Adjusted Mutual Information[11], Normalized mutual information[11])
|- data
|  |- CausalPairGenerator (Generate causal pairs)
|  |- AcyclicGraphGenerator (Generate FCM-based graphs)
|  |- load_dataset (load standard benchmark datasets)
|- causality
|  |- graph (methods for graph inference)
|  |  |- CGNN[12]
|  |  |- PC[13]
|  |  |- GES[13]
|  |  |- GIES[13]
|  |  |- LiNGAM[13]
|  |  |- CAM[13]
|  |  |- GS[23]
|  |  |- IAMB[24]
|  |  |- MMPC[25]
|  |  |- SAM[26]
|  |  |- CCDr[27]
|  |
|  |- pairwise (methods for pairwise inference)
|     |- ANM[14] (Additive Noise Model)
|     |- IGCI[15] (Information Geometric Causal Inference)
|     |- RCC[16] (Randomized Causation Coefficient)
|     |- NCC[17] (Neural Causation Coefficient)
|     |- GNN[12] (Generative Neural Network -- Part of CGNN )
|     |- Bivariate fit (Baseline method of regression)
|     |- Jarfo[20]
|     |- CDS[20]
|     |- RECI[28]
|- metrics (Implements the metrics for graph scoring)
|  |- Precision Recall
|  |- SHD
|  |- SID [29]
|- utils
   |- Settings -> SETTINGS class (hardware settings)
   |- loss -> MMD loss [21, 22] & various other loss functions
   |- io -> for importing data formats
   |- graph -> graph utilities


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  • [29] Structural Intervention Distance (SID) for Evaluating Causal Graphs, Jonas Peters, Peter Bühlmann: https://arxiv.org/abs/1306.1043

Indices and tables


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