scpi: Uncertainty Quantification for Synthetic Control Methods

scpi: Uncertainty Quantification for Synthetic Control Methods

The synthetic control method offers a way to quantify the effect of an intervention using weighted averages of untreated units to approximate the counterfactual outcome that the treated unit(s) would have experienced in the absence of the intervention. This method is useful for program evaluation and causal inference in observational studies. We introduce the software package scpi for prediction and inference using synthetic controls, implemented in Python, R, and Stata. For point estimation or prediction of treatment effects, the package offers an array of (possibly penalized) approaches leveraging the latest optimization methods. For uncertainty quantification, the package offers the prediction interval methods introduced by Cattaneo, Feng, and Titiunik (2021) and Cattaneo, Feng, Palomba, and Titiunik (2025b). The paper includes numerical illustrations and a comparison with other synthetic control software.

Matias Cattaneo, Yingjie Feng, Filippo Palomba, Rocío Titiunik

https://www.jstatsoft.org/article/view/v113i01

OJS/PKP

14 JULI 2025

FAJAR BAGUS W

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