Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
arXiv 26 Dec 2023 · Econometrics
arXiv:2312.16307 · PDF · DOI · OpenAlex · Extracted main text
We consider the setting of synthetic control methods (SCMs), a canonical approach used to estimate the treatment effect on the treated in a panel data setting. We shed light on a frequently overlooked but ubiquitous assumption made in SCMs of "overlap": a treated unit can be written as some combination -- typically, convex or linear combination -- of the units that remain under control. We show that if units select their own interventions, and there is sufficiently large heterogeneity between units that prefer different interventions, overlap will not hold. We address this issue by proposing a framework which incentivizes units with different preferences to take interventions they would not normally consider. Specifically, leveraging tools from information design and online learning, we propose a SCM that incentivizes exploration in panel data settings by providing incentive-compatible intervention recommendations to units. We establish this estimator obtains valid counterfactual estimates without the need for an a priori overlap assumption. We extend our results to the setting of synthetic interventions, where the goal is to produce counterfactual outcomes under all interventions, not just control. Finally, we provide two hypothesis tests for determining whether unit overlap holds for a given panel dataset.
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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 | Anish Agarwal and Keegan Harris and Justin Whitehouse and Zhiwei Ste… (2023) Adaptive Principal Component Regression with Applications to Panel Data self | 0.935 | 11 | 7 | 82% |
| 2 | Agarwal, Anish and Shah, Devavrat and Shen, Dennis (2020) Synthetic interventions self | 0.843 | 10 | 6 | 60% |
| 3 | Yishay Mansour and Aleksandrs Slivkins and Vasilis Syrgkanis (2020) Bayesian Incentive-Compatible Bandit Exploration self | 0.843 | 5 | 4 | 60% |
| 4 | Keegan Harris and Anish Agarwal and Chara Podimata and Zhiwei Steven… (2024) Strategyproof Decision-Making in Panel Data Settings and Beyond self | 0.737 | 3 | 2 | 100% |
| 5 | 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 | 0.693 | 5 | 1 | 100% |
| 6 | Mark Sellke and Aleksandrs Slivkins (2023) The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity | 0.644 | 2 | 2 | 100% |
| 7 | Mark Sellke (2023) Incentivizing Exploration with Linear Contexts and Combinatorial Actions | 0.644 | 2 | 2 | 100% |
| 8 | Abadie, Alberto and Gardeazabal, Javier (2003) The economic costs of conflict: A case study of the Basque Country | 0.511 | 2 | 1 | 100% |
| 9 | Agarwal, Anish and Shah, Devavrat and Shen, Dennis (2020) On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls self | 0.511 | 2 | 1 | 100% |
| 10 | Muhammad J. Amjad and Devavrat Shah and Dennis Shen (2018) Robust Synthetic Control | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Adaptive Principal Component Regression with Applications to Panel Data | 0.405 | 1 | 1 |