Metadata-Version: 2.1
Name: JustCause
Version: 0.4
Summary: Comparing methods for causality analysis in a fair and just way.
Home-page: https://github.com/inovex/justcause/
Author: Maximilian Franz, Florian Wilhelm
Author-email: Maximilian.Franz@inovex.de, Florian.Wilhelm@inovex.de
License: mit
Project-URL: Documentation, https://justcause.readthedocs.io/
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        <div style="text-align:center">
        <p align="center">
        <img alt="JustCause logo" src="https://justcause.readthedocs.io/en/latest/_static/logo.png">
        </p>
        </div>
        
        <br/>
        
        # Introduction
        
        Evaluating causal inference methods in a scientifically thorough way is a cumbersome and error-prone task.
        To foster good scientific practice **JustCause** provides a framework to easily:
        
        1. evaluate your method using common data sets like IHDP, IBM ACIC, and others;
        2. create synthetic data sets with a generic but standardized approach;
        3. benchmark your method against several baseline and state-of-the-art methods.
        
        Our *cause* is to develop a framework that allows you to compare methods for causal inference
        in a fair and *just* way. JustCause is a work in progress and new contributors are always welcome.
        
        # Installation
        
        If you just want to use the functionality of JustCause, install it with:
        ```
        pip install justcause
        ```
        Consider using [conda] to create a virtual environment first.
        
        Developers that want to develop and contribute own algorithms and data sets to the JustCause framework, should:
        
        1. clone the repository and change into the directory
           ```
           git clone https://github.com/inovex/justcause.git
           cd justcause
           ```
        
        2. create an environment `justcause` with the help of [conda],
           ```
           conda env create -f environment.yaml
           ```
        3. activate the new environment with
           ```
           conda activate justcause
           ```
        4. install `justcause` with:
           ```
           python setup.py install # or `develop`
           ```
        
        Optional and needed only once after `git clone`:
        
        5. install several [pre-commit] git hooks with:
           ```
           pre-commit install
           ```
           and checkout the configuration under `.pre-commit-config.yaml`.
           The `-n, --no-verify` flag of `git commit` can be used to deactivate pre-commit hooks temporarily.
        
        
        # Related Projects & Resources
        
         1. [causalml]: causal inference with machine learning algorithms in Python
         2. [DoWhy]: causal inference using graphs for identification
         3. [EconML]: Heterogeneous Effect Estimation in Python
         4. [awesome-list]: A very extensive list of causal methods and respective code
         5. [IBM-Causal-Inference-Benchmarking-Framework]: Causal Inference Benchmarking Framework by IBM
         6. [CausalNex]: Bayesian Networks to combine machine learning and domain expertise for causal reasoning.
        
        ## Note
        
        This project has been set up using [PyScaffold] 3.2.2 and the [dsproject extension] 0.4.
        For details and usage information on PyScaffold see https://pyscaffold.org/.
        
        
        [conda]: https://docs.conda.io/
        [pre-commit]: https://pre-commit.com/
        [Jupyter]: https://jupyter.org/
        [Google style]: http://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings
        [PyScaffold]: https://pyscaffold.org/
        [dsproject extension]: https://github.com/pyscaffold/pyscaffoldext-dsproject
        [causalml]: https://github.com/uber/causalml
        [DoWhy]: https://github.com/Microsoft/dowhy
        [EconML]: https://github.com/microsoft/EconML
        [awesome-list]: https://github.com/rguo12/awesome-causality-algorithms
        [IBM-Causal-Inference-Benchmarking-Framework]: https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework
        [CausalNex]: https://causalnex.readthedocs.io/
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
