IDEAS home Printed from https://ideas.repec.org/r/arx/papers/1909.03489.html

Multiway Cluster Robust Double/Debiased Machine Learning

Citations

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


Cited by:

  1. Jieun Lee, 2026. "Double/Debiased Machine Learning for Functional-Form-Robust Spatial Autoregression," Papers 2608.22706, arXiv.org.
  2. Richard M. Crowley & Yun Lou & Samuel T. Tan & Liandong Zhang, 2025. "Misinformation regulations: early evidence on corporate social media strategy," Review of Accounting Studies, Springer, vol. 30(4), pages 3558-3595, December.
  3. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
  4. Liu, Nan & Liu, Yanbo & Sasaki, Yuya, 2026. "Estimation and inference for causal functions with multi-way clustered data," Journal of Econometrics, Elsevier, vol. 253(C).
  5. Kaicheng Chen & Harold D. Chiang, 2026. "Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence," Papers 2602.11333, arXiv.org, revised Sep 2026.
  6. Bryan S. Graham & Fengshi Niu & James L. Powell, 2020. "Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression," Papers 2012.08444, arXiv.org, revised Mar 2021.
  7. Shu Guan & Mingshan Li, 2025. "Government Procurement and Corporate Green Innovation: Evidence from Dual Machine Learning," SAGE Open, , vol. 15(4), pages 21582440251, November.
  8. Bowen Shi & Xiaojie Mao & Mochen Yang & Bo Li, 2026. "What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning," Information Systems Research, INFORMS, vol. 37(2), pages 1259-1275, June.
  9. Wu, Liangpeng & Tang, Yujing & Meng, Lei & Zhu, Qingyuan & Zhou, Dequn, 2025. "Navigating ESG rating divergence: Implications for labor investment efficiency and firm adaptation strategy," Global Finance Journal, Elsevier, vol. 67(C).
  10. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Jackknife Inference with Two-Way Clustering," Working Paper 1516, Economics Department, Queen's University.
  11. Mo, Shu & Liu, Xinghua, 2025. "Strengthening the resilience of urban energy systems amid renewable energy transition: A new method based on double machine learning," Energy Policy, Elsevier, vol. 206(C).
  12. Aslan Bakirov & Francesco Del Prato & Paolo Zacchia, 2026. "TWICE: Tree-based Wage Inference with Clustering and Estimation," Papers 2601.00776, arXiv.org.
  13. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
  14. Pedro Cadahia Delgado, 2026. "Across-Design Uncertainty in Short Pricing Panels: Inference and Identification," Papers 2608.21334, arXiv.org, revised Sep 2026.
  15. Yingzheng Yan & Liupeng Chen & Aoqi Xu & Yuanzhu Wei, 2026. "Government Audits and Rural Revitalization: Causal Evidence From China," SAGE Open, , vol. 16(1), pages 21582440251, February.
  16. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2023. "Inference for High-Dimensional Exchangeable Arrays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1595-1605, July.
  17. Han, Dongri & Wang, Ruiqi & Yuan, Yijia & Xiao, Deheng, 2026. "Research on the impact of ultra-high voltage transmission on urban carbon neutral technology innovation: An empirical test based on double machine learning method," Energy Economics, Elsevier, vol. 154(C).
  18. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
  19. Zikun Ye & Zhiqi Zhang & Dennis J. Zhang & Heng Zhang & Renyu Zhang, 2026. "Deep Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence," Management Science, INFORMS, vol. 72(7), pages 5611-5634, July.
  20. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
  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. Zhang, Shengwu & Han, Liyan, 2026. "Low-altitude infrastructure and economic growth: Evidence from general aviation airports," Transport Policy, Elsevier, vol. 175(C).
  23. 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.
  24. Kangqi Jiang & Lulu Chen & Jiayun Li & Xinyi Du, 2025. "The risk effects of corporate digitalization: exacerbate or mitigate?," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-19, December.
  25. Chiang, Harold D. & Li, Jiatong & Sasaki, Yuya, 2025. "Algorithmic Subsampling Under Multiway Clustering," Econometric Theory, Cambridge University Press, vol. 41(1), pages 79-122, February.
  26. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
  27. Jia, Fu & Hu, Dingguo & Chen, Lujie, 2026. "How does corporate greenwashing affect firm resilience –Causal inference utilizing dual machine learning techniques," Technological Forecasting and Social Change, Elsevier, vol. 226(C).
  28. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.