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Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Citations

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Cited by:

  1. Sabzi, Behnam & Wang, Chuyue & Wang, Kailai & Li, Zhixia & Zhang, Yunpeng & Cheng, Long & Lim, Gino, 2026. "Heterogeneous impacts of system and vehicle security concern on autonomous vehicle adoption: A causal machine learning approach," Journal of choice modelling, Elsevier, vol. 59(C).
  2. Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
  3. 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.
  4. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
  5. 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.
  6. 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.
  7. Justin Young & Eleanor Wiske Dillon, 2026. "Reevaluating Causal Estimation Methods with Data from a Product Release," Papers 2601.11845, arXiv.org, revised Oct 2026.
  8. Elia Lapenta & Anthony Strittmatter & Pedro Vergara Merino, 2026. "A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity," Papers 2607.06412, arXiv.org.
  9. 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.
  10. 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.
  11. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
  12. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Nov 2025.
  13. 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.
  14. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
  15. 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.
  16. 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.
  17. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
  18. 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.
  19. 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.
  20. Galiani, Sebastian & Quistorff, Brian, 2024. "Assessing external validity in practice," Research in Economics, Elsevier, vol. 78(3).
  21. 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.
  22. 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.
  23. Daniele Ballinari & Alexander Wehrli, 2024. "Semiparametric inference for impulse response functions using double/debiased machine learning," Papers 2411.10009, arXiv.org, revised Dec 2025.
  24. 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.
  25. 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.
  26. 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.
  27. 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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