IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2312.16307.html
   My bibliography  Save this paper

Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration

Author

Listed:
  • Daniel Ngo
  • Keegan Harris
  • Anish Agarwal
  • Vasilis Syrgkanis
  • Zhiwei Steven Wu

Abstract

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.

Suggested Citation

  • Daniel Ngo & Keegan Harris & Anish Agarwal & Vasilis Syrgkanis & Zhiwei Steven Wu, 2023. "Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration," Papers 2312.16307, arXiv.org, revised Feb 2024.
  • Handle: RePEc:arx:papers:2312.16307
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2312.16307
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhummad Amjad & Vishal Misra & Devavrat Shah & Dennis Shen, 2019. "mRSC: Multi-dimensional Robust Synthetic Control," Papers 1905.06400, arXiv.org, revised Sep 2019.
    2. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296, Elsevier.
    3. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    4. Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
    5. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    6. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    7. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    8. Orley Ashenfelter & David Card, 1984. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," Working Papers 554, Princeton University, Department of Economics, Industrial Relations Section..
    9. Emir Kamenica, 2019. "Bayesian Persuasion and Information Design," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 249-272, August.
    10. Jiafeng Chen, 2023. "Synthetic Control as Online Linear Regression," Econometrica, Econometric Society, vol. 91(2), pages 465-491, March.
    11. Qiu Hongxiang & Carone Marco & Luedtke Alex, 2022. "Individualized treatment rules under stochastic treatment cost constraints," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 480-493, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Keegan Harris & Anish Agarwal & Chara Podimata & Zhiwei Steven Wu, 2022. "Strategyproof Decision-Making in Panel Data Settings and Beyond," Papers 2211.14236, arXiv.org, revised Dec 2023.
    2. Anish Agarwal & Vasilis Syrgkanis, 2022. "Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime," Papers 2210.11003, arXiv.org.
    3. Marco D. Huesch & Truls Østbye & Michael K. Ong, 2012. "Measuring The Effect Of Policy Interventions At The Population Level: Some Methodological Concerns," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1234-1249, October.
    4. Wright, Austin L. & Sonin, Konstantin & Driscoll, Jesse & Wilson, Jarnickae, 2020. "Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 544-554.
    5. Jean‐Louis Combes & Xavier Debrun & Alexandru Minea & René Tapsoba, 2018. "Inflation Targeting, Fiscal Rules and the Policy Mix: Cross‐effects and Interactions," Economic Journal, Royal Economic Society, vol. 128(615), pages 2755-2784, November.
    6. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    7. Lin, Boqiang & Li, Xuehui, 2011. "The effect of carbon tax on per capita CO2 emissions," Energy Policy, Elsevier, vol. 39(9), pages 5137-5146, September.
    8. Cammeraat, Emile & Jongen, Egbert L. W. & Koning, Pierre, 2017. "Preventing NEETs during the Great Recession: The Effects of a Mandatory Activation Program for Young Welfare Recipients," IZA Discussion Papers 11090, Institute of Labor Economics (IZA).
    9. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    10. Senftleben-König, Charlotte, 2014. "Product market deregulation and employment outcomes: Evidence from the German retail sector," SFB 649 Discussion Papers 2014-013, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Daniel Kuehnle & Christoph Wunder, 2017. "The Effects of Smoking Bans on Self‐Assessed Health: Evidence from Germany," Health Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 321-337, March.
    12. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    13. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    14. Ahlfeldt, Gabriel M. & Nitsch, Volker & Wendland, Nicolai, 2019. "Ease vs. noise: Long-run changes in the value of transport (dis)amenities," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    15. Rösner, Anja & Haucap, Justus & Heimeshoff, Ulrich, 2020. "The impact of consumer protection in the digital age: Evidence from the European Union," International Journal of Industrial Organization, Elsevier, vol. 73(C).
    16. René TAPSOBA & Alexandru MINEA & Jean-Louis COMBES, 2012. "Inflation Targeting and Fiscal Rules: Do Interactions and Sequence of Adoption Matter?," Working Papers 201223, CERDI.
    17. Ciani Emanuele & Fisher Paul, 2019. "Dif-in-Dif Estimators of Multiplicative Treatment Effects," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-10, January.
    18. Erlend E. Bø & Joel Slemrod & Thor O. Thoresen, 2015. "Taxes on the Internet: Deterrence Effects of Public Disclosure," American Economic Journal: Economic Policy, American Economic Association, vol. 7(1), pages 36-62, February.
    19. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Aug 2024.
    20. Jean-Louis Combes & Mr. Xavier Debrun & Alexandru Minea & Rene Tapsoba, 2014. "Inflation Targeting and Fiscal Rules: Do Interactions and Sequencing Matter?," IMF Working Papers 2014/089, International Monetary Fund.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2312.16307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.