Background: When conducting a randomized controlled trial, it is common to specify in advance the statistical analyses that will be used to analyze the data. Typically, these analyses will involve adjusting for small imbalances in baseline covariates. However, this poses a dilemma, as adjusting for too many covariates can hurt precision more than it helps, and it is often unclear which covariates are predictive of outcome prior to conducting the experiment. Objectives: This article aims to produce a covariate adjustment method that allows for automatic variable selection, so that practitioners need not commit to any specific set of covariates prior to seeing the data. Results: In this article, we propose the “leave-one-out potential outcomes” estimator. We leave out each observation and then impute that observation’s treatment and control potential outcomes using a prediction algorithm such as a random forest. In addition to allowing for automatic variable selection, this estimator is unbiased under the Neyman–Rubin model, generally performs at least as well as the unadjusted estimator, and the experimental randomization largely justifies the statistical assumptions made.

中文翻译：

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循环估计器：调整随机实验中的协变量。

背景：在进行随机对照试验时，通常会预先指定将用于分析数据的统计分析。通常，这些分析将涉及调整基线协变量中的小不平衡。然而，这带来了两难境地，因为调整过多的协变量可能会损害精度而不是帮助，而且在进行实验之前通常不清楚哪些协变量可以预测结果。目标：本文旨在产生一种协变量调整方法，该方法允许自动选择变量，因此从业者在查看数据之前无需承诺任何特定的协变量集。结果：在本文中，我们提出了“留一法潜在结果”估计量。我们省略了每个观察结果，然后使用随机森林等预测算法估算该观察结果的处理和控制潜在结果。除了允许自动变量选择之外，该估计量在 Neyman-Rubin 模型下是无偏的，通常至少与未调整的估计量一样好，并且实验随机化在很大程度上证明了所做的统计假设是正确的。