IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1712.03553.html
   My bibliography  Save this paper

RNN-based counterfactual prediction, with an application to homestead policy and public schooling

Author

Listed:
  • Jason Poulos
  • Shuxi Zeng

Abstract

This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit outcomes to learn a useful representation for predicting future outcomes. The learned representation of control units is then applied to the treated units for predicting counterfactual outcomes. RNNs are specifically structured to exploit temporal dependencies in panel data, and are able to learn negative and nonlinear interactions between control unit outcomes. We apply the method to the problem of estimating the long-run impact of U.S. homestead policy on public school spending.

Suggested Citation

  • Jason Poulos & Shuxi Zeng, 2017. "RNN-based counterfactual prediction, with an application to homestead policy and public schooling," Papers 1712.03553, arXiv.org, revised May 2021.
    • Handle: RePEc:arx:papers:1712.03553
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1712.03553
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernando Ferreira & Joseph Gyourko, 2009. "Do Political Parties Matter? Evidence from U.S. Cities," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(1), pages 399-422.
    2. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    3. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    4. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    5. Ferman, Bruno & Pinto, Cristine Campos de Xavier, 2016. "Revisiting the synthetic control estimator," Textos para discussão 421, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Stefan Klößner & Ashok Kaul & Gregor Pfeifer & Manuel Schieler, 2018. "Comparative politics and the synthetic control method revisited: a note on Abadie et al. (2015)," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-11, December.
    7. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    8. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacob Goldin & Julian Nyarko & Justin Young, 2022. "Forecasting Algorithms for Causal Inference with Panel Data," Papers 2208.03489, arXiv.org, revised Apr 2024.
    2. Amit Yaniv-Rosenfeld & Elizaveta Savchenko & Ariel Rosenfeld & Teddy Lazebnik, 2023. "Scheduling BCG and IL-2 Injections for Bladder Cancer Immunotherapy Treatment," Mathematics, MDPI, vol. 11(5), pages 1-13, February.

    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. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    2. Irene Botosaru & Bruno Ferman, 2019. "On the role of covariates in the synthetic control method," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 117-130.
    3. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.
    4. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2021. "Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 549-584, December.
    5. Klößner, Stefan & Pfeifer, Gregor, 2015. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113207, Verein für Socialpolitik / German Economic Association.
    6. Peter Backus & Thien Nguyen, 2021. "The Effect of the Sex Buyer Law on the Market for Sex, Sexual Health and Sexual Violence," Economics Discussion Paper Series 2106, Economics, The University of Manchester.
    7. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    8. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    9. Felipe Filgueiras, Elias Cavalcante-Filho, Rodrigo de Losso, José Roberto Savoia, 2019. "Law Change in a Regulated Sector Impacts Other Regulated Sectors: Evidence from Brazil," Working Papers, Department of Economics 2019_27, University of São Paulo (FEA-USP).
    10. Becker, Maike & Pfeifer, Gregor & Schweikert, Karsten, 2021. "Price Effects of the Austrian Fuel Price Fixing Act: A Synthetic Control Study," Energy Economics, Elsevier, vol. 97(C).
    11. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 115373, University Library of Munich, Germany.
    12. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 120662, University Library of Munich, Germany, revised 27 Mar 2024.
    13. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    14. Ferman, Bruno & Pinto, Cristine, 2017. "Placebo Tests for Synthetic Controls," MPRA Paper 78079, University Library of Munich, Germany.
    15. Ferman, Bruno & Pinto, Cristine, 2016. "Revisiting the Synthetic Control Estimator," MPRA Paper 73982, University Library of Munich, Germany.
    16. Muhammad Jehangir Amjad & Devavrat Shah & Dennis Shen, 2017. "Robust Synthetic Control," Papers 1711.06940, arXiv.org.
    17. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2020. "Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method," IREA Working Papers 202005, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.
    18. Maximiliano Marzetti & Rok Spruk, 2023. "Long-Term Economic Effects of Populist Legal Reforms: Evidence from Argentina," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(1), pages 60-95, March.
    19. Breinlich, Holger & Leromain, Elsa & Novy, Dennis & Sampson, Thomas, 2020. "Voting with their money: Brexit and outward investment by UK firms," European Economic Review, Elsevier, vol. 124(C).
    20. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.

    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:1712.03553. 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.