Metadata-Version: 2.1
Name: pycausalmatch
Version: 0.0.2
Summary: Causal Impact of an intervention integrated with control group selection
Home-page: https://github.com/unbiasedmodeler/pycausalmatch
Author: Rishi Jumani
Author-email: unbiased.modeler@gmail.com
License: UNKNOWN
Description: 
        # pycausalmatch
        
        pycausalmatch is a Python library for causal inference integrated with the
        process of selecting suitable control groups
        
        
        #### Description
        
        The functionality that has been implemented so far is essentially a Python translation of the
        features available in the R library: https://github.com/klarsen1/MarketMatching (v.1.1.7 - as of Dec 2020),
        which combines 2 packages: https://github.com/dafiti/causalimpact and https://github.com/DynamicTimeWarping/dtw-python
        
        The DTW package is used for selection of most suitable control groups.
        
        The R library has a detailed README.
        
        The causal impact from this Python version matches the impact for the test market ('CPH') in the example
        in the R library, as shown in the plots in the `starter_example` notebook.
        
        This is still an **alpha release** - I'm in the process of adding more features, and fixing
        all the bugs soon!
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install pycausalmatch.
        
        ```bash
        pip install pycausalmatch
        ```
        
        ## Usage
        
        ```python
        from pycausalmatch import R_MarketMatching as rmm
        
        rmm.best_matches(**kwargs) # returns
        rmm.inference(**kwargs) # returns
        
        ```
        
        This package has only been tested for ** a single test market** (I will test it for multiple test markets soon)
        
        
        ## Example Use case
        
        I've added an example on the causal impact of Prop 99 in California in the notebook `prop_99_example`
        under the examples folder. I will keep updating this example as I develop the library further.
        
        
        
        
        ## TODOs
        
        - [ ] Improve README!
        
        - [ ] Add more examples (Prop 99 - CA)
        
        - [ ] add tests
        
        - [ ] add statistical inference
        
        - [ ] use software project structure template
        
        - [ ] Integrate into an MLOps workflow
        
        - [ ] Add parallel execution (I plan to use Bodo)
        
        - [ ] Add Streamlit and Dash app
        
        - [ ] switch to https://github.com/WillianFuks/tfcausalimpact
        
        - [ ] add remaining functionality of the R package
        
        
        
        
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
