论文标题
条件平衡测试：提高敏感性和特异性，预后协变量
Conditional Balance Tests: Increasing Sensitivity and Specificity With Prognostic Covariates
论文作者
论文摘要
研究人员经常使用协变量平衡测试来评估是否随机分配“ AS-IF”处理变量。但是，标准测试可能不会阐明因果推断的关键条件：治疗分配和潜在结果的独立性。我们专注于影响平衡测试敏感性和特异性的关键因素：协变量的预后，即预测潜在结果的程度。我们根据平均值的协变量差异加权总和提出了“有条件平衡测试”，其中权重是来自观察到的协变量结果的标准化回归的系数。我们的理论和模拟表明，当潜在结果不平衡时，这种方法相对于其他全球测试的力量增加，同时由于对无关的协变量不平衡而限制了虚假拒绝。
Researchers often use covariate balance tests to assess whether a treatment variable is assigned "as-if" at random. However, standard tests may shed no light on a key condition for causal inference: the independence of treatment assignment and potential outcomes. We focus on a key factor that affects the sensitivity and specificity of balance tests: the extent to which covariates are prognostic, that is, predictive of potential outcomes. We propose a "conditional balance test" based on the weighted sum of covariate differences of means, where the weights are coefficients from a standardized regression of observed outcomes on covariates. Our theory and simulations show that this approach increases power relative to other global tests when potential outcomes are imbalanced, while limiting spurious rejections due to imbalance on irrelevant covariates.
