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.
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.
Python 2.7 and 3.6
- javabridge>=1.0.11
- pandas
- numpy
- JDK 1.8
- pydot (Optional)
- GraphViz (Optional)
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...
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.
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.
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.
The pre-installed py-causal Docker image is also available at Docker Hub
