A Python package for causal modelling and inference with stochastic causal programming

This project is developed in collaboration with the Centre for Advanced Research Computing, University College London.

• Ricardo Silva (rbas-ucl)
• Jialin Yu (jialin-yu)
• Will Graham (willGraham01)
• Matthew Scroggs (mscroggs)
• Matt Graham (matt-graham)

Centre for Advanced Research Computing, University College London ([email protected])

causalprog requires Python 3.11–3.13.

We recommend installing in a project specific virtual environment. To install the latest development version of causalprog using pip in the currently active environment run

pip install git+https://github.com/UCL/causalprog.git

Alternatively create a local clone of the repository with

git clone https://github.com/UCL/causalprog.git

and then install in editable mode by running

pip install -e .

Tests can be run across all compatible Python versions in isolated environments using tox by running

tox

To run tests manually in a Python environment with pytest installed run

pytest tests

again from the root of the repository.

For more information about the testing suite, please see the documentation page.

The MkDocs HTML documentation can be built locally by running

tox -e docs

from the root of the repository. The built documentation will be written to site.

Alternatively to build and preview the documentation locally, in a Python environment with the optional docs dependencies installed, run

mkdocs serve

This work was funded by Engineering and Physical Sciences Research Council (EPSRC).