Matias D. Cattaneo, Yingjie Feng, Filippo Palomba, Rocio Titiunik
arXiv 12 Feb 2022 · Statistics — Methodology · publishedJournal of Statistical Software (2025) · 4 citations (OpenAlex)
arXiv:2202.05984 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cattaneo, M. D., Feng, Y., Palomba, F., and Titiunik, R (2022) Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption self | 1.000 | 7 | 4 | 100% |
| 2 | Cattaneo, M. D., Feng, Y., and Titiunik, R (2021) Prediction Intervals for Synthetic Control Methods self | 0.928 | 4 | 3 | 100% |
| 3 | Abadie, A (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects | 0.843 | 3 | 3 | 100% |
| 4 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.644 | 2 | 2 | 100% |
| 5 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls | 0.644 | 2 | 2 | 100% |
| 6 | Domahidi, A., Chu, E., and Boyd, S (2013) ECOS: An SOCP solver for embedded systems, in | 0.644 | 2 | 2 | 100% |
| 7 | Fu, A., Narasimhan, B., and Boyd, S (2020) CVXR: An R Package for Disciplined Convex Optimization | 0.644 | 2 | 2 | 100% |
| 8 | Abadie, A., and Cattaneo, M. D (2018) Econometric Methods for Program Evaluation self | 0.405 | 1 | 1 | 100% |
| 9 | Abadie, A., and Gardeazabal, J (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.405 | 1 | 1 | 100% |
| 10 | Abadie, A., and L'Hour, J (2021) A Penalized Synthetic Control Estimator for Disaggregated Data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Supplement to “Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption” | 0.644 | 2 | 2 |