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
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
Requires-Dist: numpy (>=1.18.1)
Requires-Dist: pandas (>=1.0.3)
Requires-Dist: dtw-python (==1.1.6)
Requires-Dist: pycausalimpact (==0.0.14)


# 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/)


