Optimal Covariate Discretization in Adaptive Randomized Experiments
研究在自适应随机实验中如何离散化协变量以提升平均处理效应估计精度，提出基于自助法的算法寻找最优离散化水平，并通过模拟和实际数据验证效果。
ABSTRACT This paper introduces a novel method to adaptively design randomized experiments. For randomized experiments with a pilot stage, or multistage experiments, Hahn et al. (2011) propose an adaptive experimental design that adjusts the next stage's propensity score based on data from the previous stages. This paper discusses how the discretization of covariates affects the precision of the estimation of the average treatment effect (ATE) through the estimated propensity score for the next stage. Also, this paper proposes an algorithm using the bootstrap technique to find the optimal level of discretization of covariates. Monte Carlo simulations and an application with actual data show that the suggested method performs well.