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

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  • 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.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa001
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    Citations

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    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. 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).
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

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