IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2501.07550.html
   My bibliography  Save this paper

disco: Distributional Synthetic Controls

Author

Listed:
  • Florian Gunsilius
  • David Van Dijcke

Abstract

The method of synthetic controls is widely used for evaluating causal effects of policy changes in settings with observational data. Often, researchers aim to estimate the causal impact of policy interventions on a treated unit at an aggregate level while also possessing data at a finer granularity. In this article, we introduce the new disco command, which implements the Distributional Synthetic Controls method introduced in Gunsilius (2023). This command allows researchers to construct entire synthetic distributions for the treated unit based on an optimally weighted average of the distributions of the control units. Several aggregation schemes are provided to facilitate clear reporting of the distributional effects of the treatment. The package offers both quantile-based and CDF-based approaches, comprehensive inference procedures via bootstrap and permutation methods, and visualization capabilities. We empirically illustrate the use of the package by replicating the results in Van Dijcke et al. (2024).

Suggested Citation

  • Florian Gunsilius & David Van Dijcke, 2025. "disco: Distributional Synthetic Controls," Papers 2501.07550, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2501.07550
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2501.07550
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. F. F. Gunsilius, 2023. "Distributional Synthetic Controls," Econometrica, Econometric Society, vol. 91(3), pages 1105-1117, May.
    2. 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.
    3. 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.
    4. Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    5. 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.
    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. Masahiro Kato & Akari Ohda, 2023. "Asymptotically Unbiased Synthetic Control Methods by Density Matching," Papers 2307.11127, arXiv.org, revised Feb 2025.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    3. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Camilla Beck Olsen & Hans Olav Melberg, 2018. "Did adolescents in Norway respond to the elimination of copayments for general practitioner services?," Health Economics, John Wiley & Sons, Ltd., vol. 27(7), pages 1120-1130, July.
    9. Niklas Potrafke & Fabian Ruthardt & Kaspar Wuthrich, 2020. "Protectionism and economic growth: Causal evidence from the first era of globalization," Papers 2010.02378, arXiv.org, revised Mar 2022.
    10. 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.
    11. Francesca Caselli & Matilde Faralli & Paolo Manasse & Ugo Panizza, 2021. "On the Benefits of Repaying," IMF Working Papers 2021/233, International Monetary Fund.
    12. Jinyong Hahn & Ruoyao Shi, 2017. "Synthetic Control and Inference," Econometrics, MDPI, vol. 5(4), pages 1-12, November.
    13. Damon Jones & Ioana Marinescu, 2022. "The Labor Market Impacts of Universal and Permanent Cash Transfers: Evidence from the Alaska Permanent Fund," American Economic Journal: Economic Policy, American Economic Association, vol. 14(2), pages 315-340, May.
    14. Dario Laudati & M. Hashem Pesaran, 2023. "Identifying the effects of sanctions on the Iranian economy using newspaper coverage," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 271-294, April.
    15. Jason Poulos & Andrea Albanese & Andrea Mercatanti & Fan Li, 2021. "Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment," Papers 2106.00788, arXiv.org.
    16. Bai, Jushan & Wang, Peng, 2024. "Causal inference using factor models," MPRA Paper 120585, University Library of Munich, Germany.
    17. Giovanni Peri & Vasil Yasenov, 2019. "The Labor Market Effects of a Refugee Wave: Synthetic Control Method Meets the Mariel Boatlift," Journal of Human Resources, University of Wisconsin Press, vol. 54(2), pages 267-309.
    18. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    19. Giovanni Peri & Derek Rury & Justin C. Wiltshire, 2024. "The Economic Impact of Migrants from Hurricane Maria," Journal of Human Resources, University of Wisconsin Press, vol. 59(6), pages 1795-1829.
    20. 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.

    More about this item

    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:2501.07550. 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.