随机实验中的协变量调整：决策与实践的统一框架
Covariate Adjustment in Randomized Experiments: A Unified Framework for Decision and Practice
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- Duke University（杜克大学）
- Columbia University（哥伦比亚大学）
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中文总结 AI 辅助
针对随机实验中协变量调整的争议，提出以“事后偏差”预后性为唯一标准的统一框架，并开发两种估计方法，为调整决策提供统一基础。
中文摘要 AI 辅助
研究者是否应在随机实验中调整协变量，以及如果调整应如何进行？文献提供了三种不同的处方：不调整，因为随机化保证了无偏性；调整结果预后性协变量以提高精度；或调整在治疗组间不平衡的协变量。这些相互矛盾的处方造成了混乱和不确定性。我们开发了一个用于决策和实践的统一框架。在给定可用信息的情况下，我们表明最优修正就是我们所谓的“事后偏差”。调整的唯一相关标准是对事后偏差的预后性；单纯的协变量不平衡或结果预后性本身都不充分。我们还表明，修正不平衡和提高精度是同一决策问题的两个方面。我们开发了两种估计方法，其中一种恢复了熟悉的调整估计量，并为其提供了新的理论依据。模拟比较了不同的协变量选择与调整策略。总体而言，我们的框架为随机实验中的协变量调整提供了统一的基础。
英文摘要
Should researchers adjust for covariates in randomized experiments, and if so, how? The literature offers three distinct prescriptions: do not adjust because randomization guarantees unbiasedness; adjust for outcome-prognostic covariates to improve precision; or adjust for covariates imbalanced between treatment arms. These competing prescriptions create confusion and uncertainty. We develop a unified framework for decision and practice. Given available information, we show that the optimal correction is what we call ex-post bias. The only relevant criterion for adjustment is prognosticity for ex-post bias; neither raw covariate imbalance nor outcome prognosticity is sufficient by itself. We also show that correcting imbalance and improving precision are two sides of the same decision problem. We develop two estimation approaches, one of which recovers familiar adjustment estimators and provides a new theoretical justification for them. Simulations compare alternative covariate-selection and adjustment strategies. Overall, our framework provides a unified foundation for covariate adjustment in randomized experiments.
