It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behavior of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of covariate-adaptive randomization procedures via comparisons to traditional uniform inference in matched designs with and without propensity score calipers and regression adjustment using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients.

翻译：在匹配观察性研究中，通常假设匹配集内的处理分配是均匀随机的，并以此分布为基础进行因果推断。这种做法忽略了匹配集中可能对处理分配分布产生影响的观察差异，而这些差异通过倾向得分的组内差异得到简洁刻画。我们通过协变量自适应随机化推断来解决该问题：该方法根据估计的倾向得分差异调整置换概率，无需排除匹配对或对结局变量建模。研究表明，当倾向得分估计可用大样本时，该检验的第一类错误率可任意接近名义水平。我们刻画了用于估计常数加性效应的均值差异估计量所对应的新随机化检验的大样本性质。同时证明，现有敏感性分析方法可有效推广至协变量自适应随机化推断。最后，通过模拟研究以及针对焊工遗传损伤和手术患者右心导管插入术的数据分析，我们在有无倾向得分卡尺和回归调整的匹配设计中比较了协变量自适应随机化程序与传统均匀推断的经验价值。