arXiv 22 Mar 2022 · Statistics — Methodology · 3 citations (OpenAlex)
arXiv:2203.11576 · PDF · DOI · OpenAlex · Extracted main text
Synthetic control methods often rely on matching pre-treatment characteristics (called predictors) of the treated unit. The choice of predictors and how they are weighted plays a key role in the performance and interpretability of synthetic control estimators. This paper proposes the use of a sparse synthetic control procedure that penalizes the number of predictors used in generating the counterfactual to select the most important predictors. We derive, in a linear factor model framework, a new model selection consistency result and show that the penalized procedure has a faster mean squared error convergence rate. Through a simulation study, we then show that the sparse synthetic control achieves lower bias and has better post-treatment performance than the un-penalized synthetic control. Finally, we apply the method to revisit the study of the passage of Proposition 99 in California in an augmented setting with a large number of predictors available.
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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 | Abadie, Alberto, Diamond, Alexis, Hainmueller, Jens (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program | 0.937 | 17 | 6 | 82% |
| 2 | Quistorff, Brian, Goldman, Matt, Thorpe, Jason (2020) Sparse Synthetic Controls: Unit-Level Counterfactuals from High-Dimensional Data | 0.874 | 5 | 2 | 100% |
| 3 | Abadie, Alberto, Vives-i-Bastida, Jaume (2022) Synthetic Controls in Action self | 0.843 | 3 | 3 | 100% |
| 4 | Abadie, Alberto, Diamond, Alexis, Hainmueller, Jens (2015) Comparative Politics and the Synthetic Control Method | 0.811 | 4 | 2 | 100% |
| 5 | Arkhangelsky, Dmitry, Athey, Susan, Hirshberg, David A., Imbens, Gui… (2021) Synthetic Difference-in-Differences | 0.737 | 4 | 3 | 50% |
| 6 | Ferman, Bruno, Pinto, Cristine (2021) Synthetic controls with imperfect pretreatment fit | 0.737 | 3 | 2 | 100% |
| 7 | Abadie, Alberto, Gardeazabal, Javier (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.405 | 1 | 1 | 100% |
| 8 | Bai, Jushan (2009) Panel Data Models With Interactive Fixed Effects | 0.405 | 1 | 1 | 100% |
| 9 | Ben-Michael, Eli, Feller, Avi, Rothstein, Jesse (2021) The Augmented Synthetic Control Method | 0.405 | 1 | 1 | 100% |
| 10 | Chetverikov, Denis, Liao, Zhipeng, Chernozhukov, Victor (2016) On cross-validated Lasso in high dimensions | 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 | 2108.02196 | 0.405 | 1 | 1 |
| 2 | Splash! Robustifying Donor Pools for Policy Studies | 0.405 | 1 | 1 |