IDEAS home Printed from https://ideas.repec.org/a/oup/emjrnl/v23y2020i2p177-191..html
   My bibliography  Save this article

Double/debiased machine learning for difference-in-differences models

Author

Listed:
  • Neng-Chieh Chang

Abstract

SummaryThis paper provides an orthogonal extension of the semiparametric difference-in-differences estimator proposed in earlier literature. The proposed estimator enjoys the so-called Neyman orthogonality (Chernozhukov et al., 2018), and thus it allows researchers to flexibly use a rich set of machine learning methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set in which the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude.

Suggested Citation

  • Neng-Chieh Chang, 2020. "Double/debiased machine learning for difference-in-differences models," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 177-191.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:2:p:177-191.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ectj/utaa001
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
    2. Havrda, Marek & Klocek, Adam, 2023. "Well-being impact assessment of artificial intelligence – A search for causality and proposal for an open platform for well-being impact assessment of AI systems," Evaluation and Program Planning, Elsevier, vol. 99(C).
    3. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    4. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    5. Bonev, Petyo & Gorkun-Voevoda, Liudmila & Knaus, Michael, 2022. "The effect of environmental policies on environmental behaviors and intrinsic motivation: evidence from the European Union," Economics Working Paper Series 2207, University of St. Gallen, School of Economics and Political Science, revised Sep 2022.
    6. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python," Papers 2104.03220, arXiv.org, revised Dec 2021.
    7. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    8. Anoop Kumar & Suresh Dodda & Navin Kamuni & Rajeev Kumar Arora, 2024. "Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets," Papers 2404.07225, arXiv.org.
    9. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    10. Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2024. "Model Averaging and Double Machine Learning," IZA Discussion Papers 16714, Institute of Labor Economics (IZA).
    11. Moshoeshoe,Ramaele Elias, 2020. "Long-Term Effects of Free Primary Education on Educational Achievement : Evidence from Lesotho," Policy Research Working Paper Series 9404, The World Bank.
    12. Martin Huber & Eva-Maria Oe{ss}, 2024. "A joint test of unconfoundedness and common trends," Papers 2404.16961, arXiv.org, revised Jun 2024.
    13. Bonev, Petyo & Gorkun-Voevoda, Liudmila & Knaus, Michael, 2022. "The Effect of Environmental Policies on Intrinsic Motivation: Evidence from the Eurobarometer Surveys," VfS Annual Conference 2022 (Basel): Big Data in Economics 264028, Verein für Socialpolitik / German Economic Association.
    14. Marcel Caesmann & Janis Goldzycher & Matteo Grigoletto & Lorenz Gschwent, 2024. "Censorship in democracy," ECON - Working Papers 446, Department of Economics - University of Zurich.
    15. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.
    17. Jizhou Wang & Jin’an He & Richard Cebula & Maggie Foley & Fangping Peng, 2024. "Mixed ownership reform, political connections, and overinvestment," American Journal of Economics and Sociology, Wiley Blackwell, vol. 83(2), pages 407-425, March.
    18. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2022. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 288-303.
    19. Nora Bearth, 2024. "Beyond Baby Blues: The Child Penalty in Mental Health in Switzerland," Papers 2410.20861, arXiv.org.
    20. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    21. Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.

    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:oup:emjrnl:v:23:y:2020:i:2:p:177-191.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.html .

    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.