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Stratification Trees for Adaptive Randomization in Randomized Controlled Trials

Citations

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Cited by:

  1. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
  2. Juan C. Yamin, 2026. "When and How to Pilot: Design Rules for Two-Wave Experiments," Papers 2607.16982, arXiv.org, revised Aug 2026.
  3. Federico A. Bugni & Mengsi Gao, 2021. "Inference under Covariate-Adaptive Randomization with Imperfect Compliance," Papers 2102.03937, arXiv.org, revised Jul 2023.
  4. Li Harrison H. & Owen Art B., 2024. "Double machine learning and design in batch adaptive experiments," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-27.
  5. Liang Jiang & Xiaobin Liu & Peter C. B. Phillips & Yichong Zhang, 2024. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 542-556, March.
  6. Ahnaf Rafi, 2023. "Efficient Semiparametric Estimation of Average Treatment Effects Under Covariate Adaptive Randomization," Papers 2305.08340, arXiv.org.
  7. Jan Kemper & Davud Rostam-Afschar, 2025. "Inference for Batched Adaptive Experiments," Papers 2512.10156, arXiv.org.
  8. Bokelmann, Björn & Lessmann, Stefan, 2025. "Heteroscedasticity-aware stratified sampling to improve uplift modeling," European Journal of Operational Research, Elsevier, vol. 325(1), pages 118-131.
  9. Bugni, Federico A. & Gao, Mengsi, 2023. "Inference under covariate-adaptive randomization with imperfect compliance," Journal of Econometrics, Elsevier, vol. 237(1).
  10. Elia Lapenta & Anthony Strittmatter & Pedro Vergara Merino, 2026. "A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity," Papers 2607.06412, arXiv.org.
  11. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2019. "Inference under covariate‐adaptive randomization with multiple treatments," Quantitative Economics, Econometric Society, vol. 10(4), pages 1747-1785, November.
  12. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jun 2026.
  13. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Feb 2026.
  14. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised Oct 2026.
  15. Ruicheng Ao & Hongyu Chen & David Simchi-Levi, 2024. "Prediction-Guided Active Experiments," Papers 2411.12036, arXiv.org, revised Nov 2024.
  16. Harrison H. Li & Art B. Owen, 2023. "Double machine learning and design in batch adaptive experiments," Papers 2309.15297, arXiv.org.
  17. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
  18. Rosenman Evan T. R. & Owen Art B., 2021. "Designing experiments informed by observational studies," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 147-171, January.
  19. Jiang, Liang & Li, Liyao & Miao, Ke & Zhang, Yichong, 2025. "Adjustments with many regressors under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 249(PB).
  20. Masahiro Kato & Akihiro Oga & Wataru Komatsubara & Ryo Inokuchi, 2024. "Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices," Papers 2403.03589, arXiv.org, revised Jun 2024.
  21. Zikai Li, 2025. "Bridging Stratification and Regression Adjustment: Batch-Adaptive Stratification with Post-Design Adjustment in Randomized Experiments," Papers 2510.22908, arXiv.org.
  22. Masahiro Kato, 2023. "Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget," Papers 2310.19788, arXiv.org, revised Mar 2024.
  23. Yichong Zhang & Xin Zheng, 2020. "Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization," Quantitative Economics, Econometric Society, vol. 11(3), pages 957-982, July.
  24. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," American Economic Review, American Economic Association, vol. 112(12), pages 3911-3940, December.
  25. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.
  26. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.
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