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Synthetic Control As Online Linear Regression

Jiafeng Chen

arXiv 17 Feb 2022 · Econometrics · publishedEconometrica (2023) · 21 citations (OpenAlex)

arXiv:2202.08426 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units' outcomes. Synthetic control on differenced data performs almost as well as oracle weighted difference-in-differences, potentially making it an attractive choice in practice. We argue that this observation further supports the use of synthetic control estimators in comparative case studies.

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Doudchenko, Nikolay and Guido W Imbens (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis, Tech1.000103100%
2Orabona, Francesco (2019) A modern introduction to online learning1.00095100%
3Hazan, Elad (2019) Introduction to online convex optimization1.00084100%
4Ferman, Bruno and Cristine Pinto (2021) Synthetic controls with imperfect pretreatment fit1.00083100%
5Bottmer, Lea, Guido Imbens, Jann Spiess, and Merrill Warnick (2021) A Design-Based Perspective on Synthetic Control Methods1.00054100%
6Abadie, Alberto (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects1.00053100%
7Hazan, Elad, Amit Agarwal, and Satyen Kale (2007) Logarithmic regret algorithms for online convex optimization1.00053100%
8Abadie, Alberto, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program0.92843100%
9Chernozhukov, Victor, Kaspar Wüthrich, and Yinchu Zhu (2021) An exact and robust conformal inference method for counterfactual and synthetic controls0.73732100%
10Viviano, Davide and Jelena Bradic (2019) Synthetic learner: model-free inference on treatments over time0.64441100%

Showing the top 10 of 43 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1A Design-Based Perspective on Synthetic Control Methods0.84333
22510.261060.69371
32108.021960.51121
4Adaptive Principal Component Regression with Applications to Panel Data0.40511
5Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration0.40511
6Sequential Synthetic Difference in Differences0.40511
7Asymptotic Properties of the Distributional Synthetic Controls0.40511
8Bandit Algorithms for Policy Learning: Methods, Implementation, and Welfare-performance0.40511
9A Relaxation Approach to Synthetic Control0.40511
10A Synthetic Control Approach to Conditional Distributional Treatment Effects0.40511