摘要翻译：
本文提出了两阶段随机对照试验的自适应随机化程序。该方法使用来自第一波实验的数据，以便确定如何在实验的第二波中分层，其中目标是最小化平均治疗效果(ATE)的估计量的方差。我们考虑从一类分层随机化过程中进行选择，我们称之为分层树：这些过程的层可以表示为决策树，跨层的处理分配概率不同。通过利用第一波估计分层树，我们同时选择使用哪些协变量进行分层，如何在这些协变量上分层，以及这些层内的分配概率。我们的主要结果表明，使用这种随机化过程和适当的估计量，在分层树类中得到的渐近方差是最小的。此外，我们提出的结果能够适应地层内的一大类分配机制，包括分层块随机化。在一个模拟研究中，我们发现我们的方法，配合一个适当的交叉验证过程，可以改善分层的特别选择。我们通过将我们的方法应用于Karlan and Wood(2017)的研究得出结论，在那里我们使用他们实验的第一波来估计分层树。
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英文标题：
《Stratification Trees for Adaptive Randomization in Randomized Controlled
  Trials》
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作者：
Max Tabord-Meehan
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  This paper proposes an adaptive randomization procedure for two-stage randomized controlled trials. The method uses data from a first-wave experiment in order to determine how to stratify in a second wave of the experiment, where the objective is to minimize the variance of an estimator for the average treatment effect (ATE). We consider selection from a class of stratified randomization procedures which we call stratification trees: these are procedures whose strata can be represented as decision trees, with differing treatment assignment probabilities across strata. By using the first wave to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata. Our main result shows that using this randomization procedure with an appropriate estimator results in an asymptotic variance which is minimal in the class of stratification trees. Moreover, the results we present are able to accommodate a large class of assignment mechanisms within strata, including stratified block randomization. In a simulation study, we find that our method, paired with an appropriate cross-validation procedure ,can improve on ad-hoc choices of stratification. We conclude by applying our method to the study in Karlan and Wood (2017), where we estimate stratification trees using the first wave of their experiment.
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PDF链接：
https://arxiv.org/pdf/1806.05127