arXiv 4 Aug 2021 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2108.02196 · PDF · DOI · OpenAlex · Extracted main text
This article studies experimental design in settings where the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be exposed to the treatment. In such settings, randomization of the treatment may result in treated and control groups with very different characteristics at baseline, inducing biases. We propose a variety of experimental non-randomized synthetic control designs (Abadie, Diamond and Hainmueller, 2010, Abadie and Gardeazabal, 2003) that select the units to be treated, as well as the untreated units to be used as a control group. Average potential outcomes are estimated as weighted averages of the outcomes of treated units for potential outcomes with treatment, and weighted averages the outcomes of control units for potential outcomes without treatment. We analyze the properties of estimators based on synthetic control designs and propose new inferential techniques. We show that in experimental settings with aggregate units, synthetic control designs can substantially reduce estimation biases in comparison to randomization of the treatment.
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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 | Alberto Abadie, Alexis Diamond \ Jens Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program self | 0.693 | 9 | 1 | 100% |
| 2 | Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien L… (2021) Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls | 0.693 | 9 | 1 | 100% |
| 3 | Nikolay Doudchenko, David Gilinson, Sean Taylor \ Nils Wernerfelt Designing Experiments with Synthetic Controls | 0.693 | 6 | 1 | 100% |
| 4 | Alberto Abadie (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects self | 0.644 | 4 | 1 | 100% |
| 5 | Nikolay Doudchenko \ Guido W Imbens (2016) Balancing, Regression, Difference-in-Differences and Synthetic Control Methods: A Synthesis | 0.644 | 4 | 1 | 100% |
| 6 | Nick Jones \ Sam Barrows (2019) Synthetic Control and Alternatives to A/B Testing at Uber | 0.644 | 4 | 1 | 100% |
| 7 | Victor Chernozhukov, Kaspar Wüthrich \ Yinchu Zhu (2021) An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls | 0.630 | 8 | 1 | 75% |
| 8 | Alberto Abadie \ Jérémy L'Hour (2021) A Penalized Synthetic Control Estimator for Disaggregated Data | 0.616 | 10 | 1 | 70% |
| 9 | Alberto Abadie \ Javier Gardeazabal (2003) The Economic Costs of Conflict: A Case Study of the Basque County | 0.585 | 3 | 1 | 100% |
| 10 | Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imben… (2021) Synthetic Difference-in-Differences | 0.585 | 3 | 1 | 100% |
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