Robert Pickett, Jennifer Hill, Sarah Cowan
arXiv 19 Mar 2026 · Statistics — Methodology
arXiv:2603.19211 · PDF · DOI · OpenAlex · Extracted main text
To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on units over time, synthetic control (SC) methods provide an opportunity to construct a valid comparison by differentially weighting control units that did not receive the treatment so that their resulting pre-treatment trajectory is similar to that of the treated unit. The hope is that this weighted “pseudo-counterfactual" can serve as a valid counterfactual in the post-treatment time period. Since its origin twenty years ago, SC has been used over 5,000 times in the literature (Web of Science, December 2025), leading to a proliferation of descriptions of the method and guidance on proper usage that is not always accurate and does not always align with what the original developers appear to have intended. As such, a number of accepted pieces of wisdom have arisen: (1) SC is robust to various implementations; (2) covariates are unnecessary, and (3) pre-treatment prediction error should guide model selection. We describe each in detail and conduct simulations that suggest, both for standard and alternative implementations of SC, that these purported truths are not supported by empirical evidence and thus actually represent misconceptions about best practice. Instead of relying on these misconceptions, we offer practical advice for more cautious implementation and interpretation of results.
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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 and Diamond, Alexis and Hainmueller, Jens (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program | 1.000 | 13 | 4 | 100% |
| 2 | Ashok Kaul and Stefan Klößner and Gregor Pfeifer and Manuel Schieler (2022) Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates | 1.000 | 6 | 4 | 100% |
| 3 | Ferman, Bruno and Pinto, Cristine and Possebom, Vitor (2020) Cherry Picking with Synthetic Controls | 1.000 | 5 | 3 | 100% |
| 4 | Abadie, Alberto (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects | 0.928 | 4 | 3 | 100% |
| 5 | Arkhangelsky, Dmitry and Athey, Susan and Hirshberg, David A. and Im… (2021) Synthetic Difference-in-Differences | 0.737 | 3 | 2 | 100% |
| 6 | Botosaru, Irene and Ferman, Bruno (2019) On the role of covariates in the synthetic control method | 0.737 | 3 | 2 | 100% |
| 7 | Ben-Michael, Eli and Feller, Avi and Rothstein, Jesse (2021) The Augmented Synthetic Control Method | 0.644 | 2 | 2 | 100% |
| 8 | Ferman, Bruno and Pinto, Cristine (2021) Synthetic controls with imperfect pretreatment fit | 0.644 | 2 | 2 | 100% |
| 9 | Jones, Damon and Marinescu, Ioana (2022) The Labor Market Impacts of Universal and Permanent Cash Transfers: Evidence from the Alaska Permanent Fund | 0.644 | 2 | 2 | 100% |
| 10 | Pekka Malo and Juha Eskelinen and Xun Zhou and Timo Kuosmanen (2024) Computing Synthetic Controls Using Bilevel Optimization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 31 scored citations.