Anish Agarwal, Devavrat Shah, Dennis Shen
arXiv 13 Jun 2020 · Econometrics · 4 citations (OpenAlex)
arXiv:2006.07691 · PDF · DOI · OpenAlex · Extracted main text
The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications. Researchers commonly justify the SC framework with a low-rank matrix factor model that assumes the potential outcomes are described by low-dimensional unit and time specific latent factors. In the recent work of [Abadie '20], one of the pioneering authors of the SC method posed the question of how the SC framework can be extended to multiple treatments. This article offers one resolution to this open question that we call synthetic interventions (SI). Fundamental to the SI framework is a low-rank tensor factor model, which extends the matrix factor model by including a latent factorization over treatments. Under this model, we propose a generalization of the standard SC-based estimators. We prove the consistency for one instantiation of our approach and provide conditions under which it is asymptotically normal. Moreover, we conduct a representative simulation to study its prediction performance and revisit the canonical SC case study of [Abadie-Diamond-Hainmueller '10] on the impact of anti-tobacco legislations by exploring related questions not previously investigated.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | A. Abadie, A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of californiaâs tobacco control program | 1.000 | 16 | 8 | 100% |
| 2 | Anish Agarwal, Devavrat Shah, and Dennis Shen (2023) On principal component regression in a high-dimensional error-in-variables setting, 2023 self | 1.000 | 14 | 4 | 100% |
| 3 | Alberto Abadie (2020) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 1.000 | 7 | 5 | 100% |
| 4 | A. Abadie and J. Gardeazabal (2003) The economic costs of conflict: A case study of the basque country | 0.843 | 3 | 3 | 100% |
| 5 | Eli Ben-Michael, Avi Feller, and Jesse Rothstein (2021) The augmented synthetic control method | 0.737 | 3 | 2 | 100% |
| 6 | David Donoho and Matan Gavish (2013) The optimal hard threshold for singular values is | 0.644 | 2 | 2 | 100% |
| 7 | Anish Agarwal, Devavrat Shah, Dennis Shen, and Dogyoon Song (2021) On robustness of principal component regression self | 0.644 | 2 | 2 | 100% |
| 8 | Roman Vershynin (2018) High-dimensional probability: An introduction with applications in data science, volume 47 | 0.644 | 2 | 2 | 100% |
| 9 | Jiaming Xu (2018) Rates of convergence of spectral methods for graphon estimation | 0.644 | 2 | 2 | 100% |
| 10 | P. Wedin (1972) Perturbation bounds in connection with singular value decomposition | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.