The CCD Causal Software suite offers easy to use software for causal discovery from large and complex biomedical datasets, applying Bayesan and constraint based algorithms. It includes a web application as well as API’s and a command line version.

Causal Web

our user-friendly web application for performing causal discovery analysis on big data using large memory servers at the Pittsburgh Supercomputing Center. Use this software if you want to quickly try out a causal discovery algorithm or if you have big data that cannot be analyzed on your local hardware.

User guide Web app

Causal Command

a Java library and command line implementation of algorithms for performing causal discovery on big data. Use this software if you are interested incorporating analysis via a shell script or in a Java-based program. The ‘Software’ button below leads to a comprehensive repository. Choose the ‘causal-cmd-x.x.x -jar-with-dependencies.jar’ from the downloads list when using this as an executable via the command line or as an API in a Java program.

User guide Software Causal Command Latest Version

Py Causal

(early release) – a python module that wraps algorithms for performing causal discovery on big data.

User guide Software Docker

(early release) – an R module that that wraps algorithms for performing causal discovery on big data.

User guide Software Docker


TETRAD is a desktop java application that can connect to outside super computing resources if necessary which creates, simulates data from, estimates, tests, predicts with, and searches for causal and statistical models. The aim of the program is to provide sophisticated methods in a friendly interface requiring very little statistical sophistication of the user and no programming knowledge.


The program contains a variety of well-tested algorithms for searching for causal explanations of data under a variety of data formats and user knowledge of the domain, for uploading large data sets, manipulating data formats, and specifying models, as well as algorithms for estimating statistical parameters, testing models, predicting from models, altering predictions using new data, and extensive facilities for simulating data from user specified models, all in a uniform drag and drop graphical user interface.

Tutorial User Guide Software Tetrad Latest Version

The TETRAD Project

The TETRAD project is hosted on GitHub and consists of two repositories: the Center for Causal Discovery (CCD)  repository (  and the TETRAD repository (  TETRAD is written in Java (92 mb of sourcecode, 51.8 mb compiled as a Java application).


The TETRAD repository also offers additional tools for comparing the accuracies of multiple algorithms on multiple data sets, as the user may select. Visualization of these comparisons is via a Web application available at!/load.


Finally, in the  CCD repository you will find API wrappers of TETRAD library of search algorithms that let you access the algorithms via Python, R, a command-line or web interface; and a tool for visualizing large complex graphs produced by TETRAD in Cytoscape.

The software currently includes:


Source Code and Software Development Project:

All source code and software development activities are here and here.

Bugs and Issues:

Please report any bugs that you might encounter on our issue trackers or respective trackers.

Mailing List:

Please also sign up for our CCD User Group listserv to receive updates on software releases, training events, hackathons, and datathons.


If you use our software in your research, please acknowledge the Center for Causal Discovery, supported by grant U54HG008540, in any papers, presentations, or other dissemination of your work.

License Agreements:

All software is open-source and released under a dual licensing model. For non-profit institutions, the software is available under the GNU General Public License (GPL) v2 license.For-profit organizations that wish to commercialize enhanced or customized versions of the software will be able to purchase a commercial license on a case-by-case basis. The GPL license permits individuals to modify the source code and to share modifications with other colleagues/investigators. Specifically, it permits the dissemination and commercialization of enhanced or customized versions as well as incorporation of the software or its pieces into other license-compatible software packages, as long as modifications or enhancements are made open source.

By using software provided by the Center for Causal Discovery, you agree that no warranties of any kind are made by Carnegie Mellon University or the University of Pittsburgh with respect to the data provided by the software or any use thereof, and the universities hereby disclaim the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. The universities shall not be liable for any claims, losses, or damages of any kind arising from the data provided by the software or any use thereof.