We propose a general framework for (multiple) conditional randomization tests that incorporate several important ideas in the recent literature. We establish a general sufficient condition on the construction of multiple conditional randomization tests under which their p-values are "independent", in the sense that their joint distribution stochastically dominates the product of uniform distributions under the null. Conceptually, we argue that randomization should be understood as the mode of inference precisely based on randomization. We show that under a change of perspective, many existing statistical methods, including permutation tests for (conditional) independence and conformal prediction, are special cases of the general conditional randomization test. The versatility of our framework is further illustrated with an example concerning lagged treatment effects in stepped-wedge randomized trials.

翻译：我们建议一个包含最近文献中若干重要想法的有条件随机测试(多重)总体框架。我们为构建多重有条件随机测试规定了一个普遍的充分条件,在这种测试中,其P值是“独立的 ”,因为其共同分布在结构上支配了无效统一分配的产物。从概念上讲,我们认为随机化应被理解为完全基于随机化的推论模式。我们表明,在观点变化的情况下,许多现有的统计方法,包括(有条件的)独立和符合预测的调整测试,是一般有条件随机化测试的特殊案例。我们框架的多功能用一个实例进一步说明了我们框架的多功能性,其中说明了在渐入选随机化的试验中滞后治疗效应。