In this issue, you will find the following articles that Journal of the American Statistical Association intended to publish in the March 2021 special issue, Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery.
“More Efficient Policy Learning via Optimal Retargeting” by Nathan Kallus
“Learning Optimal Distributionally Robust Individualized Treatment Rules” by Weibin Mo, Zhengling Qi, and Yufeng Liu
“Discussion of Kallus and Mo, Qi, and Liu: New Objectives for Policy Learning” by Stijn Vansteelandt and Oliver Dukes
“Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning” by Sijia Li, Xiudi Li, and Alex Luedtke
“Discussion of Kallus (2020) and Mo et al. (2020)” by Muxuan Liang and Ying-Qi Zhao
“Rejoinder: New Objectives for Policy Learning” by Nathan Kallus
“Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules” by Weibin Mo, Zhengling Qi, and Yufeng Liu
“Statistical Inference for Online Decision Making via Stochastic Gradient Descent” by Haoyu Chen, Wenbin Lu, and Rui Song
TheTheory andMethods article “Statistical Inference for Online Decision Making: In a Contextual Bandit Setting” by Haoyu Chen, Wenbin Lu, and Rui Song was not intended to publish as part of Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery.
The publisher apologizes for these errors.