# pysyncon  [](https://codecov.io/gh/sdfordham/pysyncon)
A python module for the synthetic control method that provides implementations of:
- Synthetic Control Method (Abadie & Gardeazabal 2003)
- Robust Synthetic Control Method (Amjad, Shah & Shen 2018)
- Augmented Synthetic Control Method (Ben-Michael, Feller & Rothstein 2021)
- Penalized Synthetic Control Method (Abadie & L'Hour 2021)
The package also provides methods for performing placebo tests and generating confidence intervals.
The implementation of the synthetic control method aims to be reconcilable with the R package [Synth](https://CRAN.R-project.org/package=Synth) and similarly the implementation of the Augmented synthetic control method and the R package [augsynth](https://github.com/ebenmichael/augsynth).
## Installation
Install it from PyPI using pip:
````bash
python -m pip install pysyncon
````
## Usage
Documentation is available on [github-pages](https://sdfordham.github.io/pysyncon/). In the examples folder are notebooks reproducing the weights from:
- The Economic Costs of Conflict: A Case Study of the Basque Country, Alberto Abadie and Javier Gardeazabal; The American Economic Review Vol. 93, No. 1 (Mar., 2003), pp. 113-132. ([notebook here](examples/basque.ipynb))
- The worked example 'Prison construction and Black male incarceration' from the last chapter of 'Causal Inference: The Mixtape' by Scott Cunningham. ([notebook here](examples/texas.ipynb))
- Comparative Politics and the Synthetic Control Method, Alberto Abadie, Alexis Diamond and Jens Hainmueller; American Journal of Political Science Vol. 59, No. 2 (April 2015), pp. 495-510. ([notebook here](examples/germany.ipynb))
## Citation
If you use this package in your research, you can cite it as below.
```
@software{pysyncon,
author = {Fordham, Stiofán},
month = dec,
title = {{pysyncon: a Python package for the Synthetic Control Method}},
url = {https://github.com/sdfordham/pysyncon},
year = {2022}
}
```
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"description": "\n\n# pysyncon  [](https://codecov.io/gh/sdfordham/pysyncon)\n\nA python module for the synthetic control method that provides implementations of:\n\n- Synthetic Control Method (Abadie & Gardeazabal 2003)\n- Robust Synthetic Control Method (Amjad, Shah & Shen 2018)\n- Augmented Synthetic Control Method (Ben-Michael, Feller & Rothstein 2021)\n- Penalized Synthetic Control Method (Abadie & L'Hour 2021)\n\nThe package also provides methods for performing placebo tests and generating confidence intervals.\n\nThe implementation of the synthetic control method aims to be reconcilable with the R package [Synth](https://CRAN.R-project.org/package=Synth) and similarly the implementation of the Augmented synthetic control method and the R package [augsynth](https://github.com/ebenmichael/augsynth).\n\n## Installation\nInstall it from PyPI using pip:\n\n````bash\npython -m pip install pysyncon\n````\n\n## Usage\n\nDocumentation is available on [github-pages](https://sdfordham.github.io/pysyncon/). In the examples folder are notebooks reproducing the weights from:\n\n- The Economic Costs of Conflict: A Case Study of the Basque Country, Alberto Abadie and Javier Gardeazabal; The American Economic Review Vol. 93, No. 1 (Mar., 2003), pp. 113-132. ([notebook here](examples/basque.ipynb))\n- The worked example 'Prison construction and Black male incarceration' from the last chapter of 'Causal Inference: The Mixtape' by Scott Cunningham. ([notebook here](examples/texas.ipynb))\n- Comparative Politics and the Synthetic Control Method, Alberto Abadie, Alexis Diamond and Jens Hainmueller; American Journal of Political Science Vol. 59, No. 2 (April 2015), pp. 495-510. ([notebook here](examples/germany.ipynb))\n\n## Citation\n\nIf you use this package in your research, you can cite it as below.\n\n```\n@software{pysyncon,\n author = {Fordham, Stiof\u00e1n},\n month = dec,\n title = {{pysyncon: a Python package for the Synthetic Control Method}},\n url = {https://github.com/sdfordham/pysyncon},\n year = {2022}\n}\n```\n",
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