In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum's framework of exact tests in randomized experiments with covariate adjustments. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially when nonlinear association or interaction among covariates is expected. Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.

翻译：本研究提出了一种基于机器学习调整的新型推断方法，用于随机对照试验。该方法在罗森鲍姆的随机实验协变量调整精确检验框架下构建。通过大量模拟实验，我们证明所提方法能够稳健控制第一类错误，并提升随机对照试验的统计效率。这一优势在实际案例中得到进一步验证。该方法具有简洁性、灵活性和鲁棒性，使其成为随机对照试验常规推断流程的有力候选方案，特别适用于协变量间存在非线性关联或交互作用的场景。该方法的应用可显著降低随机对照试验（如III期临床试验）所需的样本规模与研究成本。