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Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Citations

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Cited by:

  1. Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
  2. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
  3. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
  4. Antonacci,Paulo & Muhammad Khudadad Chattha, 2024. "Evaluation of Door-to-Door Tax Enforcement Strategy in Indonesia," Policy Research Working Paper Series 10901, The World Bank.
  5. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
  6. Justin Young & Eleanor Wiske Dillon, 2026. "Reevaluating Causal Estimation Methods with Data from a Product Release," Papers 2601.11845, arXiv.org, revised May 2026.
  7. Elia Lapenta & Anthony Strittmatter & Pedro Vergara Merino, 2026. "A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity," Papers 2607.06412, arXiv.org.
  8. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
  9. Kelvin Mulungu & Zewdu Ayalew Abro & Wambui Beatrice Muriithi & Menale Kassie & Miachael Kidoido & Subramanian Sevgan & Samira Mohamed & Chrysantus Tanga & Fathiya Khamis, 2024. "One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 261-279, February.
  10. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
  11. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Nov 2025.
  12. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
  13. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
  14. Emre Tepe, 2024. "A random forests-based hedonic price model accounting for spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 26(4), pages 511-540, October.
  15. Na Bo & Ying Ding, 2026. "Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 32(1), pages 1-26, March.
  16. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
  17. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised Nov 2025.
  18. Yusuke Narita & Shota Yasui & Kohei Yata, 2018. "Efficient Counterfactual Learning from Bandit Feedback," Cowles Foundation Discussion Papers 2155, Cowles Foundation for Research in Economics, Yale University.
  19. Galiani, Sebastian & Quistorff, Brian, 2024. "Assessing external validity in practice," Research in Economics, Elsevier, vol. 78(3).
  20. Hugo Bodory & Martin Huber & Michael Lechner, 2024. "The Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2053-2078, October.
  21. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
  22. Daniele Ballinari & Alexander Wehrli, 2024. "Semiparametric inference for impulse response functions using double/debiased machine learning," Papers 2411.10009, arXiv.org, revised Dec 2025.
  23. 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.
  24. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2026. "Regression adjustment for estimating distributional treatment effects in randomized controlled trials," Econometric Reviews, Taylor & Francis Journals, vol. 45(1), pages 2-17, January.
  25. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
  26. Fengshi Niu & Harsha Nori & Brian Quistorff & Rich Caruana & Donald Ngwe & Aadharsh Kannan, 2022. "Differentially Private Estimation of Heterogeneous Causal Effects," Papers 2202.11043, arXiv.org.
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