SCPI

The scpi package provides Python, R and Stata implementations of estimation and inference procedures for synthetic control methods.

This work was supported by the National Science Foundation through grants SES-1947805, SES-2019432, and SES-2241575, and by the National Institutes of Health through grant R01 GM072611-16.

Queries and Requests

Please email: scpi_pkg@googlegroups.com

Python Implementation

To install/update in Python type:

pip install scpi_pkg

Help: PyPI repository.
Replication: py-script, plot illustration, data.
Illustration Staggered Adoption: py-script, plot illustration.

R Implementation

To install/update in R from CRAN type:

install.packages('scpi')

Help: R Manual, CRAN repository.
Replication: R-script, plot illustration, data.
Illustration Staggered Adoption: R-script, plot illustration.

Stata Implementation

The Stata implementation relies on Python, which needs to be available in the system.

How to install Python

There are at least two ways to install Python:

Download and install Python directly from https://realpython.com/installing-python/.
Download and install Anaconda for Windows, macOS, or Linux.

After Python is installed, please run the following two commands via the Python command line:

pip install luddite
pip install scpi_pkg

How to link Stata and Python

Stata (16.0 or newer) and Python (>=3.8) can be linked following the official tutorial on the Stata blog.

To install/update in Stata type:

net install grc1leg, from("http://www.stata.com/users/vwiggins/") replace force
net install scpi, from(https://raw.githubusercontent.com/nppackages/scpi/master/stata) replace force

Help: scdata, scest, scpi, scplot.
Replication files: do-file, plot illustration, data.
Illustration Staggered Adoption: do-file, plot illustration.

References

Software and Implementation

Cattaneo, Feng, Palomba and Titiunik (2024): scpi: Uncertainty Quantification for Synthetic Control Methods.
Journal of Statistical Software, forthcoming.

Technical and Methodological

Cattaneo, Feng, Palomba and Titiunik (2024): Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption.
Working paper.
Supplemental
Cattaneo, Feng and Titiunik (2021): Prediction Intervals for Synthetic Control Methods.
Journal of the American Statistical Association 116(536): 1865-1880.
Supplemental