Eli Ben-Michael, Avi Feller, Jesse Rothstein
arXiv 10 Nov 2018 · Statistics — Methodology · publishedJournal of the American Statistical Association (2021) · 67 citations (OpenAlex)
arXiv:1811.04170 · PDF · DOI · OpenAlex · Extracted main text
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package.
appendix boundary found by appendix_command · 54% 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 | Ferman, B. and C. Pinto (2018) Synthetic controls with imperfect pre-treatment fit | 1.000 | 5 | 4 | 100% |
| 2 | Abadie, A., A. Diamond, and J. Hainmueller (2015) Comparative Politics and the Synthetic Control Method | 0.980 | 17 | 6 | 94% |
| 3 | Doudchenko, N. and G. W. Imbens (2017) Difference-In-Differences and Synthetic Control Methods: A Synthesis | 0.969 | 11 | 6 | 91% |
| 4 | Abadie, A. and J. L'Hour (2018) A penalized synthetic control estimator for disaggregated data | 0.941 | 6 | 5 | 83% |
| 5 | Xu, Y (2017) Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models | 0.941 | 6 | 5 | 83% |
| 6 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program | 0.933 | 16 | 7 | 81% |
| 7 | Athey, S., M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi (2017) Matrix Completion Methods for Causal Panel Data Models | 0.928 | 4 | 3 | 100% |
| 8 | Abadie, A. and J. Gardeazabal (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.843 | 4 | 3 | 75% |
| 9 | Robbins, M., J. Saunders, and B. Kilmer (2017) A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Inte… | 0.843 | 5 | 4 | 60% |
| 10 | Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wa… (2019) Synthetic difference in differences | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 59 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.