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Ensemble Methods for Causal Effects in Panel Data Settings

Citations

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Cited by:

  1. Alabrese, Eleonora & Edenhofer, Jacob & Fetzer, Thiemo & Wang, Shizhuo, 2020. "Measuring the Regional Economic Cost of Brexit: Evidence as of 2026," CAGE Online Working Paper Series 486, Competitive Advantage in the Global Economy (CAGE).
  2. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  3. Newell, Richard G. & Prest, Brian C. & Sexton, Steven E., 2021. "The GDP-Temperature relationship: Implications for climate change damages," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
  4. Tzvetan Moev, 2025. "Correlated Synthetic Controls," Papers 2507.08918, arXiv.org.
  5. Rong J. B. Zhu, 2023. "Synthetic Regressing Control," Papers 2306.02584, arXiv.org, revised Jan 2026.
  6. Fetzer, Thiemo & Wang, Shizhou, 2020. "Measuring the Regional Economic Cost of Brexit: Evidence up to 2019," The Warwick Economics Research Paper Series (TWERPS) 1280, University of Warwick, Department of Economics.
  7. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
  8. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
  9. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  10. Mehmet Caner Agostino Capponi Mihailo Stojnic, 2025. "Model-Estimation-Free, Dense, and High Dimensional Consistent Precision Matrix Estimators," Papers 2507.04663, arXiv.org, revised Dec 2025.
  11. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  12. Lastauskas, Povilas & Stakėnas, Julius, 2024. "Labor market policies in high- and low-interest rate environments: Evidence from the euro area," Economic Modelling, Elsevier, vol. 141(C).
  13. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
  14. Jesús Fernández-Villaverde, 2021. "Has machine learning rendered simple rules obsolete?," European Journal of Law and Economics, Springer, vol. 52(2), pages 251-265, December.
  15. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
  16. Surender Kumar & Madhu Khanna, 2019. "Temperature and production efficiency growth: empirical evidence," Climatic Change, Springer, vol. 156(1), pages 209-229, September.
  17. Povilas Lastauskas & Julius Stakénas, 2019. "Does It Matter When Labor Market Reforms Are Implemented? The Role of the Monetary Policy Environment," CESifo Working Paper Series 7844, CESifo.
  18. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  19. Philippe Goulet Coulombe & Karin Klieber, 2025. "Opening the Black Box of Local Projections," Papers 2505.12422, arXiv.org, revised Jul 2025.
  20. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
  21. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.
  22. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  23. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.
  24. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2025. "Density forecasts of inflation: A quantile regression forest approach," European Economic Review, Elsevier, vol. 178(C).
  25. Matthew Smith & Francisco Alvarez, 2025. "Machine Learning for Applied Economic Analysis: Gaining Practical Insights," Working Papers 2025-03, FEDEA.
  26. Hollingsworth, Alex & Wing, Coady, 2020. "Tactics for design and inference in synthetic control studies: An applied example using high-dimensional data," SocArXiv fc9xt, Center for Open Science.
  27. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
  28. Gabriel Loumeau & Christian Stettler, 2021. "Fiscal Autonomy and Self-Determination," CESifo Working Paper Series 9445, CESifo.
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