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Further information on causal inference, the code, usage and more can be found on the [docs](https://causal-testing-framework.readthedocs.io/en/latest/)
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To install the latest stable release of the Causal Testing Framework:
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``pip install causal-testing-framework``
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or if you want to install with the development packages/tools:
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``pip install causal-testing-framework[dev]``
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Alternatively, you can install directly via source:
pip install -e .# For editable install, useful for development work
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```
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For more information on how to use the Causal Testing Framework, please refer to our [documentation](https://causal-testing-framework.readthedocs.io/en/latest/?badge=latest).
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## How to Cite
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If you use our framework in your work, please cite the following:
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``This research has used version X.Y.Z (software citation) of the
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Causal Testing Framework (paper citation).``
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The paper citation should be the Causal Testing Framework [paper](https://dl.acm.org/doi/10.1145/3607184),
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and the software citation should contain the specific Figshare [DOI](https://orda.shef.ac.uk/articles/software/CITCOM_Software_Release/24427516) of the version used in your work.
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<details>
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<summary><b>BibTeX Citations</b></summary>
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<details>
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<summary>Paper</summary>
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```
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@ARTICLE{Clark_etal_2023,
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author = {Clark, Andrew G. and Foster, Michael and Prifling, Benedikt and Walkinshaw, Neil and Hierons, Robert M.
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and Schmidt, Volker and Turner, Robert D.},
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title = {Testing Causality in Scientific Modelling Software},
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year = {2023},
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publisher = {Association for Computing Machinery},
author = {Foster, Michael and Clark, Andrew G. and Somers, Richard and Wild, Christopher and Allian, Farhad and Hierons, Robert M. and Wagg, David and Walkinshaw, Neil},
To contribute to our work, please ensure the following:
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1.[Fork the repository](https://help.github.com/articles/fork-a-repo/) into your own GitHub account, and [clone](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) it to your local machine.
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2.[Create a new branch](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-and-deleting-branches-within-your-repository) in your forked repository. Give this branch an appropriate name, and create commits that describe the changes.
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3.[Push your changes](https://docs.github.com/en/get-started/using-git/pushing-commits-to-a-remote-repository) to your new branch in your remote fork, compare with `CausalTestingFramework/main`, and ensure any conflicts are resolved.
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4. Create a draft [pull request](https://docs.github.com/en/get-started/quickstart/hello-world#opening-a-pull-request) from your branch, and ensure you have [linked](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/autolinked-references-and-urls) it to any relevant issues in your description.
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We use the [unittest]() module to develop our tests and the [pytest](https://pytest.org/en/latest/) framework as our test discovery, [pylint](https://pypi.org/project/pylint/) for our code analyser, and [black](https://pypi.org/project/black/) for our code formatting.
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To find the other (optional) developer dependencies, please check `pyproject.toml`.
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## Acknowledgements
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The Causal Testing Framework is supported by the UK's Engineering and Physical Sciences Research Council (EPSRC),
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with the project name [CITCOM](https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T030526/1) - "_Causal Inference for Testing of Computational Models_"
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1. Ensure all project dependencies are installed by running `pip install .` in the top level directory
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(instructions are provided in the project README).
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2. Change directory to `causal_testing/examples/poisson-line-process`.
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3. Run the command `python test_poisson_process.py`
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3. Run the command `python example_poisson_process.py`
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This should print a series of causal test results and produce two CSV files. `intensity_num_shapes_results_random_1000.csv` corresponds to table 1, and `width_num_shapes_results_random_1000.csv` relates to our findings regarding the relationship of width and `P_u`.
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