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.
Release files for scpi-pkg 4.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scpi_pkg-4.0.0.tar.gz | 59.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scpi_pkg-4.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 121.0 kB
Release files / scpi_pkg-4.0.0.tar.gz
| Download URL | scpi_pkg-4.0.0.tar.gz |
|---|---|
| Size | 59.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / scpi_pkg-4.0.0-py3-none-any.whl
| Download URL | scpi_pkg-4.0.0-py3-none-any.whl |
|---|---|
| Size | 61.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
231a3a8de3576cbf3ee1c23081c6bebb8def106745550597fcbf36b7e4a3791d
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 2, 2026.
Transparency log
