IDEAS home Printed from https://ideas.repec.org/r/oup/emjrnl/v23y2020i2p177-191..html

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

Citations

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


Cited by:

  1. Bo Xu & Rengui Sun & Cunhu Xi & Zhaoping Wang, 2025. "Digital governance and the low-carbon transition: evidence from double machine learning," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-14, December.
  2. Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
  3. Yixiao Sun & Haitian Xie & Yuhang Zhang, 2025. "Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation," Papers 2503.11375, arXiv.org, revised Sep 2025.
  4. 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.
  5. Nora Bearth, 2024. "Beyond Baby Blues: The Child Penalty in Mental Health in Switzerland," Papers 2410.20861, arXiv.org, revised May 2025.
  6. Marcel Caesmann & Matteo Grigoletto & Lorenz Gschwent, 2024. "Censorship in democracy," ECON - Working Papers 446, Department of Economics - University of Zurich, revised Sep 2025.
  7. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2025.
  8. 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.
  9. 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.
  10. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2025. "Model Averaging and Double Machine Learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 249-269, April.
  11. 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.
  12. 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.
  13. 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.
  14. Franziska Zimmert & Michael Zimmert, 2024. "Part‐time subsidies and maternal reemployment: Evidence from a difference‐in‐differences analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1149-1171, September.
  15. Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org, revised Jun 2025.
  16. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2024. "Semiparametric Bayesian Difference-in-Differences," Papers 2412.04605, arXiv.org, revised Jun 2025.
  17. 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).
  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. 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).
  20. 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.
  21. 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.
  22. Martin Huber & Eva-Maria Oe{ss}, 2024. "A joint test of unconfoundedness and common trends," Papers 2404.16961, arXiv.org, revised Jun 2024.
  23. Dor Leventer, 2025. "Conditional Triple Difference-in-Differences," Papers 2502.16126, arXiv.org, revised Jun 2025.
  24. 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.
  25. Lucas Z. Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org, revised Dec 2025.
  26. 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.
  27. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
  28. Liu, Yulin & Wei, Haoran, 2025. "Will alleviating energy poverty enhance social trust in China? An approach based on dual machine learning modeling," Energy Economics, Elsevier, vol. 147(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.