FenTechSolutions / Causaldiscoverytoolbox

The Causal Discovery Toolbox is a 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.

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

Check out the documentation here

Please cite us if you use our software

A tutorial is available here

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

pip install cdt

Docker images

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

Branch	master	dev
Python 3.6 - CPU
Python 3.6 - GPU

Installation

The packages requires a python version >=3.5, as well as some libraries listed in 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.

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

Install PyTorch

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: http://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

Check out the package structure and more info on the package itself here.

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 file for debian based distributions. The r-requirements file notes all of the R packages used by the toolbox.

Overview

General package structure

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

Hardware and algorithm settings

The toolbox has a SETTINGS class that defines the hardware settings. Those settings are unique and their default parameters are defined in cdt/utils/Settings.

These parameters are accessible and overridable via accessing the class:

import cdt
cdt.SETTINGS

Moreover, the hardware parameters are detected and defined automatically (including number of GPUs, CPUs, available optional packages) at the import of the package using the cdt.utils.Settings.autoset_settings method, run at startup.

The graph class

The whole package revolves around using the DiGraph and Graph classes from the networkx package.

References

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• [19] Zhang, K., & Hyvärinen, A. (2009, June). On the identifiability of the post-nonlinear causal model. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (pp. 647-655). AUAI Press.
• [20] Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.
• [21] Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.
• [22] Li, Y., Swersky, K., & Zemel, R. (2015). Generative moment matching networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15) (pp. 1718-1727).
• [23] Margaritis D (2003). Learning Bayesian Network Model Structure from Data . Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153
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• [26] Kalainathan, Diviyan & Goudet, Olivier & Guyon, Isabelle & Lopez-Paz, David & Sebag, Michèle. (2018). SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning.
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• [28] Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., & Schoelkopf, B. (2018, March). Cause-Effect Inference by Comparing Regression Errors. In International Conference on Artificial Intelligence and Statistics (pp. 900-909).
• [29] Structural Intervention Distance (SID) for Evaluating Causal Graphs, Jonas Peters, Peter Bühlmann: https://arxiv.org/abs/1306.1043