causalprog

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.

About

Project team

• Ricardo Silva (rbas2015)
• Jialin Yu (jialin-yu)
• Will Graham (willGraham01)
• Matthew Scroggs (mscroggs)
• Matt Graham (matt-graham)

Research software engineering contact

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

Getting Started

Prerequisites

causalprog requires Python 3.11–3.13.

Installation

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 .

Running tests

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.

Building documentation

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

Acknowledgements

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