[38] Xinwei Ma, Yuya Sasaki, and Yulong Wang. Testing limited overlap. working paper, 2023.
[39] Xinwei Ma and Jingshen Wang. Robust inference using inverse probability weighting. Journal of the American Statistical Association, 115(532):1851-1860, 2020.
[40] Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, and Philip S Thomas. Offline contextual bandits with high probability fairness guarantees. In Advances in Neural Information Processing Systems, volume 32, pages 14922-14933, 2019.
[41] Hervé Moulin. Fair division in the internet age. Annual Review of Economics, 11:407-441, 2019.
[42] Whitney K Newey. Semiparametric efficiency bounds. Journal of Applied Econometrics, 5(2):99-135, 1990.
[43] Vishakha Patil, Ganesh Ghalme, Vineet Nair, and Yadati Narahari. Achieving fairness in the stochastic multi-armed bandit problem. Journal of Machine Learning Research, 22(1):7885-7915, 2021.
[44] Stuart J Pocock and Richard Simon. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics, 31(1):103-115, 1975.
[45] David S Robertson, Kim May Lee, Boryana C López-Kolkovska, and Sofia S Villar. Response-adaptive randomization in clinical trials: From myths to practical considerations. Statistical Science, 38(2):185-208, 2023.
[46] William F Rosenberger. *Randomized Urn Models and Sequential Design*. Taylor & Francis, 2002.
[47] William F Rosenberger and Feifang Hu. Maximizing power and minimizing treatment failures in clinical trials. Clinical Trials, 1(2):141-147, 2004.
[48] William F Rosenberger and John M Lachin. The use of response-adaptive designs in clinical trials. Controlled Clinical Trials, 14(6):471-484, 1993.
[49] William F Rosenberger, AN Vidyashankar, and Deepak K Agarwal. Covariate-adjusted response-adaptive designs for binary response. Journal of Biopharmaceutical Statistics, 11(4):227-236, 2001.
[50] David Simchi-Levi and Chonghuan Wang. Multi-armed bandit experimental design: Online decision-making and adaptive inference. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, volume 206, pages 3086–3097, 2023.
[51] Max Tabord-Meehan. Stratification trees for adaptive randomization in randomized controlled trials. Review of Economic Studies, 90(5):2646-2673, 2023.
[52] Peter F Thall and J Kyle Wathen. Practical bayesian adaptive randomisation in clinical trials. European Journal of Cancer, 43(5):859-866, 2007.
[53] Yevgen Tymofyeyev, William F Rosenberger, and Feifang Hu. Implementing optimal allocation in sequential binary response experiments. Journal of the American Statistical Association, 102(477):224-234, 2007.
[54] Aad W. van der Vaart. Asymptotic Statistics. Cambridge University Press, New York, 1998.
[55] Sofia S Villar, Jack Bowden, and James Wason. Multi-armed bandit models for the optimal design of clinical trials: Benefits and challenges. Statistical Science, 30(2):199, 2015.
[56] Sofia S Villar, Jack Bowden, and James Wason. Response-adaptive designs for binary responses: How to offer patient benefit while being robust to time trends? Pharmaceutical Statistics, 17(2):182-197, 2018.
[57] Sofia S Villar and William F Rosenberger. Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking gittins index rule. Biometrics, 74(1):49-57, 2018.