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Note 2023-02-26: This project uses a very old version of Tetrad and a method of connecting Python to Java, Javabridge, that's proven sometimes buggy and hard to install on some platforms, and so we are no longer recommending it. Please consider using py-tetrad instead. Py-tetrad uses JPype to bridge Python and Java, which has already shown itself to be much easier to install and use cross-platform. Also, it allows one to use the most recent version of Tetrad, and it has been well-tested.

py-causal

Python APIs for causal modeling algorithms developed by the University of Pittsburgh/Carnegie Mellon University Center for Causal Discovery.

This code is distributed under the LGPL 2.1 license.

Requirements:

Python 2.7 and 3.6

  • javabridge>=1.0.11
  • pandas
  • numpy
  • JDK 1.8
  • pydot (Optional)
  • GraphViz (Optional)

Installation overview:

We have found two approaches to be useful:

  • Direct python installation with pip, possibly including use of Jupyter. This approach is likely best for users who have Python installed and are familiar with installing Python modules.
  • Installation via Anaconda, which installs Python and related utilities.

Directions for both approaches are given below...

Installation with pip

First install Java 8 or higher and Python 2.7 or higher.

If you do not have pip installed already, try these instructions.

Once pip is installed, execute these commands

pip install -U numpy
pip install -U pandas
pip install -U javabridge
pip install -U pydot # optional
pip install -U GraphViz # optional

Note: you also need to install the GraphViz engine by following these instructions.

We have observed that on some OS X installations, pydot may provide the following response Couldn't import dot_parser, loading of dot files will not be possible.

If you see this, try the following

 pip uninstall pydot
 pip install pyparsing==1.5.7
 pip install pydot

Then, from within the py-causal directory, run the following command:

python setup.py install

or use the pip command:

pip install git+git://github.com/bd2kccd/py-causal

After running this command, enter a python shell and attempt the following imports:

import pandas as pd
import pydot
from pycausal import search as s

Finally, try to run the python example

python py-causal-fges-continuous-example.py

Be sure to run this from within the py-causal directory.

This program will create a file named tetrad.svg, which should be viewable in any SVG capable program. If you see a causal graph, everything is working correctly.

Running Jupyter/IPython

We have found Jupyter notebooks to be helpful. (Those who have run IPython in the past should know that Jupyter is simply a new name for IPython). To add Jupyter to your completed python install, simply run

pip -U jupyter
jupyter notebook

and then load one of the Jupyter notebooks found in this installation.

Anaconda/Jupyter

First install Java 8 or higher and Python 2.7 or higher.

Installing Python with Anaconda and Jupyter may be easier for some users:

Then run the following to configure anaconda

conda install javabridge
conda install pandas  
conda install numpy
conda install pydot
conda install graphviz 
conda install -c https://conda.anaconda.org/chirayu pycausal 
jupyter notebook

and then load one of the Jupyter notebooks.

Docker Image

The pre-installed py-causal Docker image is also available at Docker Hub

Citation

DOI