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scpi: Uncertainty Quantification for Synthetic Control Methods

Author

Listed:
  • Matias D. Cattaneo
  • Yingjie Feng
  • Filippo Palomba
  • Rocio Titiunik
Abstract
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 (2022). The paper includes numerical illustrations and a comparison with other synthetic control software.

Suggested Citation

  • Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "scpi: Uncertainty Quantification for Synthetic Control Methods," Papers 2202.05984, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2202.05984
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    References listed on IDEAS

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    1. Azeem M. Shaikh & Panos Toulis, 2021. "Randomization Tests in Observational Studies With Staggered Adoption of Treatment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1835-1848, October.
    2. Ricardo Masini & Marcelo C. Medeiros, 2021. "Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1773-1788, October.
    3. Kathleen T. Li, 2020. "Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2068-2083, December.
    4. Eli Ben‐Michael & Avi Feller & Jesse Rothstein, 2022. "Synthetic controls with staggered adoption," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 351-381, April.
    5. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    6. Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2021. "Prediction Intervals for Synthetic Control Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1865-1880, October.
    7. Michael W. Robbins & Jessica Saunders & Beau Kilmer, 2017. "A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 109-126, January.
    8. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    9. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    10. Alberto Abadie, 2021. "Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects," Journal of Economic Literature, American Economic Association, vol. 59(2), pages 391-425, June.
    11. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    12. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    13. Alberto Abadie & Jérémy L’Hour, 2021. "A Penalized Synthetic Control Estimator for Disaggregated Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1817-1834, October.
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    Cited by:

    1. Manuel Funke & Moritz Schularick & Christoph Trebesch, 2023. "Populist Leaders and the Economy," American Economic Review, American Economic Association, vol. 113(12), pages 3249-3288, December.
    2. Carrillo-Maldonado, Paul & Arias, Karla & Zanoni, Wladimir & Cruz, Zoe, 2024. "Local socioeconomic impacts of large-scale mining projects in Ecuador: The case of Fruta del Norte," Resources Policy, Elsevier, vol. 89(C).
    3. Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption," Papers 2210.05026, arXiv.org, revised Oct 2024.
    4. Ursula Muench & Armin Nassehi & Joe Kaeser & Knut Bergmann & Matthias Diermeier & Florian Dorn & David Gstrein & Florian Neumeier & Manuel Funke & Moritz Schularick & Christoph Trebesch & Kerim Peren , 2024. "Wohlstand in Gefahr? Ursachen und Folgen von Populismus," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 77(03), pages 03-32, March.

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