In clinical trials and causal inference, it is often critical to balance treatment allocation over influential covariates. In big data era, the number of covariates is usually very large, among which only a small fraction of them are influential to the response variable due to sparsity. However, existing studies assume that all influential covariates are known, fixed and given. In this article, we propose a procedure that can select the influential covariates from a diverging number of candidates and keep the allocation balanced among the important covariates simultaneously. Under mild regularity conditions, we show that the proposed procedure can pick out important covariates and balance treatment allocation among the important covariates consistently. Further, balancing treatment allocation can help the selection of important covariates, whereas picking out important covariates can make the randomization more efficient. Numerical studies support our theoretical discoveries for the proposed procedure. We also apply our method to a virtual redesign dataset of advertising vehicle choosing and show the advantages of the proposed procedure.

中文翻译：

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临床试验中变量选择的协变量自适应随机化

在临床试验和因果推断中，平衡治疗分配与有影响的协变量通常至关重要。在大数据时代，协变量的数量通常非常多，其中由于稀疏性，只有一小部分对响应变量有影响。然而，现有研究假设所有有影响的协变量都是已知的、固定的和给定的。在本文中，我们提出了一种程序，该程序可以从不同数量的候选者中选择有影响的协变量，并同时保持重要协变量之间的分配平衡。在温和的正则性条件下，我们表明所提出的程序可以挑选出重要的协变量并一致地平衡重要协变量之间的治疗分配。此外，平衡治疗分配可以帮助选择重要的协变量，而挑选出重要的协变量可以使随机化更有效。数值研究支持我们对拟议程序的理论发现。我们还将我们的方法应用于广告车辆选择的虚拟重新设计数据集，并展示了所提出程序的优势。