We consider the problem of constructing multiple conditional randomization tests. They may test different causal hypotheses but always aim to be nearly independent, allowing the randomization p-values to be interpreted individually and combined using standard methods. We start with a simple, sequential construction of such tests, and then discuss its application to three problems: evidence factors for observational studies, lagged treatment effect in stepped-wedge trials, and spillover effect in randomized trials with interference. We compare the proposed approach with some existing methods using simulated and real datasets. Finally, we establish a general sufficient condition for constructing multiple nearly independent conditional randomization tests.

翻译：我们研究构建多重条件随机化检验的问题。这些检验可针对不同因果假设进行测试，但始终旨在保持近乎独立性，从而允许随机化p值被单独解读，并通过标准方法进行整合。首先，我们提出一种简洁的序列化构建方法，随后讨论其在三个问题中的应用：观察性研究的证据因子、阶梯设计试验中的滞后处理效应，以及存在干扰的随机试验中的溢出效应。我们通过模拟数据集与真实数据集，将所提方法与现有方法进行对比。最后，我们建立了构建多重近独立条件随机化检验的一般充分条件。