SCPI_PKG

The scpi_pkg package provides Python implementations of estimation and inference procedures for Synthetic Control methods.

Authors

Matias D. Cattaneo, Princeton University (matias.d.cattaneo@gmail.com)

Yingjie Feng, Tsinghua University (fengyingjiepku@gmail.com)

Filippo Palomba, Princeton University (filippo.palomba19@gmail.com)

Rocio Titiunik, Princeton University (rocio.titiunik@gmail.com)

Website

https://nppackages.github.io/scpi/

Installation

To install/update use pip

pip install scpi_pkg

Usage

from scpi_pkg.scdata import scdata
from scpi_pkg.scdataMulti import scdataMulti
from scpi_pkg.scest import scest
from scpi_pkg.scpi import scpi
from scpi_pkg.scplot import scplot
from scpi_pkg.scplotMulti import scplotMulti

• Replication: Germany reunification example.

Dependencies

• cvxpy (>= 1.1.18)
• dask (>= 2021.04.0)
• ecos (>= 2.0.7)
• luddite (>= 1.0.2)
• numpy (>= 1.20.1)
• pandas (>= 1.5.0)
• plotnine (>= 0.8.0)
• scikit-learn (>= 0.24.1)
• scipy (>= 1.7.1)
• statsmodels (>= 0.12.2)

References

For overviews and introductions, see nppackages website.

Software and Implementation

• Cattaneo, Feng, Palomba, and Titiunik (2025): scpi: Uncertainty Quantification for Synthetic Control Methods. Journal of Statistical Software 113(1): 1-38.

Technical and Methodological

• Cattaneo, Feng, Palomba, and Titiunik (2027): Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption. Review of Economics and Statistics, forthcoming.
• Cattaneo, Feng, and Titiunik (2021): Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association 116(536): 1865-1880.