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https://github.com/FenTechSolutions/CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
https://github.com/FenTechSolutions/CausalDiscoveryToolbox
algorithm causal-discovery causal-inference causal-models causality graph graph-structure-recovery inference machine-learning python toolbox
Last synced: 10 days ago
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Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
- Host: GitHub
- URL: https://github.com/FenTechSolutions/CausalDiscoveryToolbox
- Owner: FenTechSolutions
- License: mit
- Created: 2017-05-31T09:12:45.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-04-02T12:12:22.000Z (7 months ago)
- Last Synced: 2024-10-29T15:34:53.002Z (10 days ago)
- Topics: algorithm, causal-discovery, causal-inference, causal-models, causality, graph, graph-structure-recovery, inference, machine-learning, python, toolbox
- Language: Python
- Homepage: https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html
- Size: 13.9 MB
- Stars: 1,121
- Watchers: 37
- Forks: 199
- Open Issues: 70
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
- StarryDivineSky - FenTechSolutions/CausalDiscoveryToolbox
README
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.
[](https://travis-ci.org/FenTechSolutions/CausalDiscoveryToolbox)
[](https://travis-ci.org/FenTechSolutions/CausalDiscoveryToolbox)
[](https://codecov.io/gh/FenTechSolutions/CausalDiscoveryToolbox)
[](https://raw.githubusercontent.com/FenTechSolutions/CausalDiscoveryToolbox/master/LICENSE.md)
[](#)
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](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html)
## [Please cite us if you use our software](https://arxiv.org/abs/1903.02278)
[A tutorial is available here](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/tutorial.html)
Install it using pip: (See more details on installation below)
```sh
pip install cdt
```
## Docker images
Docker images are available, including all the dependencies, and enabled functionalities:
| Branch | master | dev |
|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------:|
| Python 3.6 - CPU | [](https://hub.docker.com/r/fentechai/cdt/) | [](https://hub.docker.com/r/fentechai/cdt-dev) |
| Python 3.6 - GPU | [](https://hub.docker.com/r/fentechai/nv-cdt/) | [](https://hub.docker.com/r/fentechai/nv-cdt-dev) |
## Installation
The packages requires a python version >=3.5, as well as some libraries listed in [requirements file](https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/requirements.txt). 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:
```sh
pip install cdt
```
Or you can also install it from source.
```sh
$ 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:
```python
import cdt
```
Check out the package structure and more info on the package itself [here](https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/documentation.md).
### 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](https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/r_requirements.txt) notes all of the R packages used by the toolbox.
Here is an example of installation script of the R packages on Ubuntu 20.04:
``` sh
apt-get -qq update
DEBIAN_FRONTEND=noninteractive apt-get install -y tzdata
apt-get -qq install dialog apt-utils -y
apt-get install apt-transport-https -y
apt-get install -qq software-properties-common -y
apt-get -qq update
apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/' -y
apt-get -qq update
apt-get -qq install r-base -y
apt-get -qq install libssl-dev -y
apt-get -qq install libgmp3-dev -y
apt-get -qq install git -y
apt-get -qq install build-essential -y
apt-get -qq install libv8-dev -y
apt-get -qq install libcurl4-openssl-dev -y
apt-get -qq install libgsl-dev -y
Rscript -e 'install.packages(c("V8"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages(c("sfsmisc"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages(c("clue"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages("https://cran.r-project.org/src/contrib/Archive/randomForest/randomForest_4.6-14.tar.gz", repos=NULL, type="source")'
Rscript -e 'install.packages(c("lattice"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages(c("devtools"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages(c("MASS"),repos="http://cran.us.r-project.org", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages("BiocManager")'
Rscript -e 'BiocManager::install(c("igraph"))'
Rscript -e 'install.packages("https://cran.r-project.org/src/contrib/Archive/fastICA/fastICA_1.2-2.tar.gz", repos=NULL, type="source")'
Rscript -e 'BiocManager::install(c("SID", "bnlearn", "pcalg", "kpcalg", "glmnet", "mboost"))'
Rscript -e 'install.packages("https://cran.r-project.org/src/contrib/Archive/CAM/CAM_1.0.tar.gz", repos=NULL, type="source")'
Rscript -e 'install.packages("https://cran.r-project.org/src/contrib/sparsebnUtils_0.0.8.tar.gz", repos=NULL, type="source")'
Rscript -e 'BiocManager::install(c("ccdrAlgorithm", "discretecdAlgorithm"))'
apt-get -qq install libxml2-dev -y
Rscript -e 'install.packages("devtools")'
Rscript -e 'library(devtools); install_github("cran/CAM"); install_github("cran/momentchi2"); install_github("Diviyan-Kalainathan/RCIT", quiet=TRUE, verbose=FALSE)'
Rscript -e 'install.packages("https://cran.r-project.org/src/contrib/Archive/sparsebn/sparsebn_0.1.2.tar.gz", repos=NULL, type="source")'
```
## 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:
```python
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
- [1] Wang, S., Nan, B., Rosset, S., & Zhu, J. (2011). Random lasso. The annals of applied statistics, 5(1), 468.
- [2] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.
- [3] Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P., & Sugiyama, M. (2014). High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1), 185-207.
- [4] Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature biotechnology, 31(8), 726-733.
- [5] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1), 389-422.
- [6] Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281-287).
- [7] Kira, K., & Rendell, L. A. (1992, July). The feature selection problem: Traditional methods and a new algorithm. In Aaai (Vol. 2, pp. 129-134).
- [8] MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4, 415–447.
- [9] Neal, R. M. (1996). Bayesian learning for neural networks. No. 118 in Lecture Notes in Statistics. New York: Springer.
- [10] Gretton, A., Bousquet, O., Smola, A., & Scholkopf, B. (2005, October). Measuring statistical dependence with Hilbert-Schmidt norms. In ALT (Vol. 16, pp. 63-78).
- [11] Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11(Oct), 2837-2854.
- [12] Goudet, O., Kalainathan, D., Caillou, P., Lopez-Paz, D., Guyon, I., Sebag, M., ... & Tubaro, P. (2017). Learning functional causal models with generative neural networks. arXiv preprint arXiv:1709.05321.
- [13] Spirtes, P., Glymour, C., Scheines, R. (2000). Causation, Prediction, and Search. MIT press.
- [14] Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems (pp. 689-696).
- [15] Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., ... & Schölkopf, B. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence, 182, 1-31.
- [16] Lopez-Paz, D., Muandet, K., Schölkopf, B., & Tolstikhin, I. (2015, June). Towards a learning theory of cause-effect inference. In International Conference on Machine Learning (pp. 1452-1461).
- [17] Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., & Bottou, L. (2017, July). Discovering causal signals in images. In Proceedings of CVPR.
- [18] Stegle, O., Janzing, D., Zhang, K., Mooij, J. M., & Schölkopf, B. (2010). Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems (pp. 1687-1695).
- [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
- [24] Tsamardinos I, Aliferis CF, Statnikov A (2003). “Algorithms for Large Scale Markov Blanket Discovery”. In “Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference”, pp. 376-381. AAAI Press.
- [25] Tsamardinos I, Aliferis CF, Statnikov A (2003). “Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations”. In “KDD ’03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining”, pp. 673-678. ACM. Tsamardinos I, Brown LE, Aliferis CF (2006). “The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm”. Machine Learning,65(1), 31-78.
- [26] Kalainathan, Diviyan & Goudet, Olivier & Guyon, Isabelle & Lopez-Paz, David & Sebag, Michèle. (2018). SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning.
- [27] Aragam, B., & Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. Journal of Machine Learning Research, 16, 2273-2328.
- [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