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Generalized Random Forests

Citations

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Cited by:

  1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  2. Uguccioni, James, 2022. "The long-run effects of parental unemployment in childhood," CLEF Working Paper Series 45, Canadian Labour Economics Forum (CLEF), University of Waterloo.
  3. Isaiah Hull & Anna Grodecka-Messi, 2022. "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers 2203.14751, arXiv.org.
  4. Grodecka, Anna & Hull, Isaiah, 2019. "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series 374, Sveriges Riksbank (Central Bank of Sweden).
  5. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
  6. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
  7. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
  8. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
  9. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
  10. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  11. Shinde, Nilesh N. & Do Valle, Stella Z. Schons & Maia, Alexandre Gori & Amacher, Gregory S., 2022. "Can an environmental policy contribute to the reduction of land conflict? Evidence from the Rural Environmental Registry (CAR) in the Brazilian Amazon," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322584, Agricultural and Applied Economics Association.
  12. Haaland, Ingar & Roth, Christopher, 2020. "Labor market concerns and support for immigration," Journal of Public Economics, Elsevier, vol. 191(C).
  13. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
  14. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
  15. Faltings, Richard & Krumer, Alex & Lechner, Michael, 2019. "Rot-Jaune-Verde. Language and Favoritism: Evidence from Swiss Soccer," Economics Working Paper Series 1915, University of St. Gallen, School of Economics and Political Science.
  16. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
  17. Carter, Michael R. & Tjernström, Emilia & Toledo, Patricia, 2019. "Heterogeneous impact dynamics of a rural business development program in Nicaragua," Journal of Development Economics, Elsevier, vol. 138(C), pages 77-98.
  18. Tianyu Du & Ayush Kanodia & Herman Brunborg & Keyon Vafa & Susan Athey, 2024. "LABOR-LLM: Language-Based Occupational Representations with Large Language Models," Papers 2406.17972, arXiv.org.
  19. Qingyuan Zhao & Dylan S. Small & Ashkan Ertefaie, 2022. "Selective inference for effect modification via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 382-413, April.
  20. Subhadeep & Mukhopadhyay & Kaijun Wang, 2020. "Breiman's "Two Cultures" Revisited and Reconciled," Papers 2005.13596, arXiv.org.
  21. Martin Hodula & Milan Szabo & Lukas Pfeifer & Martin Melecky, 2022. "Cooling the Mortgage Loan Market: The Effect of Recommended Borrower-Based Limits on New Mortgage Lending," Working Papers 2022/3, Czech National Bank.
  22. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  23. Maria Dimakopoulou & Zhengyuan Zhou & Susan Athey & Guido Imbens, 2017. "Estimation Considerations in Contextual Bandits," Papers 1711.07077, arXiv.org, revised Dec 2018.
  24. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  25. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  26. Di Fang & Michael R. Thomsen & Rodolfo M. Nayga & Aaron M. Novotny, 2019. "WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 83-105, July.
  27. Jonathan A. Cook & Saad Siddiqui, 2020. "Random forests and selected samples," Bulletin of Economic Research, Wiley Blackwell, vol. 72(3), pages 272-287, July.
  28. Macours, Karen & Behaghel, Luc & Gignoux, Jérémie, 2020. "Social learning in agriculture: does smallholder heterogeneity impede technology diffusion in Sub-Saharan Africa?," CEPR Discussion Papers 15220, C.E.P.R. Discussion Papers.
  29. Farbmacher, Helmut & Kögel, Heinrich & Spindler, Martin, 2021. "Heterogeneous effects of poverty on attention," Labour Economics, Elsevier, vol. 71(C).
  30. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
  31. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
  32. Alexander Hanbo Li & Jelena Bradic, 2019. "Censored Quantile Regression Forests," Papers 1902.03327, arXiv.org.
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