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Quasi-oracle estimation of heterogeneous treatment effects
[TensorFlow: A system for large-scale machine learning]

Citations

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Cited by:

  1. Dylong, Patrick & Uebelmesser, Silke, 2024. "Biased beliefs about immigration and economic concerns: Evidence from representative experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 217(C), pages 453-482.
  2. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
  3. Keyon Vafa & Susan Athey & David M. Blei, 2024. "Estimating Wage Disparities Using Foundation Models," Papers 2409.09894, arXiv.org.
  4. Vinish Shrestha, 2024. "Heterogeneous Impacts of ACA-Medicaid Expansion on Insurance and Labor Market Outcomes in the American South," Working Papers 2024-08, Towson University, Department of Economics, revised Jun 2024.
  5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  6. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Sep 2024.
  7. Miquel Oliu-Barton & Bary S. R. Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B. Wolff, 2022. "The effect of COVID certificates on vaccine uptake, health outcomes, and the economy," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  8. Weicong Lyu & Jee-Seon Kim & Youmi Suk, 2023. "Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 3-36, February.
  9. Hyung G. Park & Danni Wu & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 397-418, July.
  10. 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).
  11. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
  12. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
  13. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
  14. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
  15. Susan Athey & Lisa K. Simon & Oskar N. Skans & Johan Vikstrom & Yaroslav Yakymovych, 2023. "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," Papers 2307.06684, arXiv.org, revised Feb 2024.
  16. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
  17. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
  18. Alicia Curth & Mihaela van der Schaar, 2023. "In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation," Papers 2302.02923, arXiv.org, revised Jun 2023.
  19. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  20. Jocteur, Bérénice-Alexia & Maume-Deschamps, Véronique & Ribereau, Pierre, 2024. "Heterogeneous Treatment Effect-based Random Forest: HTERF," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
  21. Daniele Ballinari & Nora Bearth, 2024. "Improving the Finite Sample Performance of Double/Debiased Machine Learning with Propensity Score Calibration," Papers 2409.04874, arXiv.org.
  22. Haupt, Johannes & Lessmann, Stefan, 2022. "Targeting customers under response-dependent costs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 369-379.
  23. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
  24. Asproudis, Elias & Gedikli, Cigdem & Talavera, Oleksandr & Yilmaz, Okan, 2024. "Returns to solar panels in the housing market: A meta learner approach," Energy Economics, Elsevier, vol. 137(C).
  25. Hua Chen & Jianing Xing & Xiaoxu Yang & Kai Zhan, 2021. "Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
  26. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
  27. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
  28. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
  29. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
  30. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  31. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
  32. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
  33. Guihua Wang & Jun Li & Wallace J. Hopp, 2022. "An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies," Management Science, INFORMS, vol. 68(5), pages 3399-3418, May.
  34. Costanza Naguib, 2023. "Is the Impact of Opening the Borders Heterogeneous?," Diskussionsschriften dp2312, Universitaet Bern, Departement Volkswirtschaft.
  35. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.
  36. Nathan Kallus, 2022. "What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment," Papers 2205.10327, arXiv.org, revised Nov 2022.
  37. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy (IfW Kiel).
  38. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
  39. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Nov 2023.
  40. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  41. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
  42. Rahul Singh, 2022. "Generalized Kernel Ridge Regression for Long Term Causal Inference: Treatment Effects, Dose Responses, and Counterfactual Distributions," Papers 2201.05139, arXiv.org.
  43. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
  44. Tomoshige Nakamura & Mihoko Minami, 2021. "Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(1), pages 1-40, January.
  45. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
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