pycausalmatch 0.0.4

Causal Impact of an intervention integrated with control group selection

pycausalmatch

pycausalmatch is a Python library for causal inference integrated with the process of selecting suitable control groups

(I plan to develop this as one would a causal inference project for Big Data with the intent of eventually deploying pipelines)

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/WillianFuks/tfcausalimpact and https://github.com/DynamicTimeWarping/dtw-python

(I switched to tfcausalimpact in v0.0.4, from pycausalimpact, which was available at https://github.com/dafiti/causalimpact, but has now been removed)

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 to install pycausalmatch.

pip install pycausalmatch

Usage

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 notebooks/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, logging ...

• 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

Project Organization (partially implemented)

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience