Many classical inferential approaches fail to hold when interference exists among the population units. This amounts to the treatment status of one unit affecting the potential outcome of other units in the population. Testing for such spillover effects in this setting makes the null hypothesis non-sharp. An interesting approach to tackling the non-sharp nature of the null hypothesis in this setup is constructing conditional randomization tests such that the null is sharp on the restricted population. In randomized experiments, conditional randomized tests hold finite sample validity. Such approaches can pose computational challenges as finding these appropriate sub-populations based on experimental design can involve solving an NP-hard problem. In this paper, we view the network amongst the population as a random variable instead of being fixed. We propose a new approach that builds a conditional quasi-randomization test. Our main idea is to build the (non-sharp) null distribution of no spillover effects using random graph null models. We show that our method is exactly valid in finite-samples under mild assumptions. Our method displays enhanced power over other methods, with substantial improvement in complex experimental designs. We highlight that the method reduces to a simple permutation test, making it easy to implement in practice. We conduct a simulation study to verify the finite-sample validity of our approach and illustrate our methodology to test for interference in a weather insurance adoption experiment run in rural China.

翻译：许多经典推断方法在总体单元间存在干扰时无法成立，即一个单元的处理状态会影响总体中其他单元的结果变量。在此背景下检验此类溢出效应会导致原假设变为非尖锐状态。解决该设定下原假设非尖锐性的一种有趣方法是构建条件随机化检验，使得原假设在受限总体中保持尖锐。在随机化实验中，条件随机化检验具有有限样本有效性。此类方法可能带来计算挑战，因为基于实验设计寻找这些适当子总体可能涉及求解NP难问题。本文将总体中的网络视为随机变量而非固定变量，提出一种构建条件拟随机化检验的新方法。核心思路是利用随机图零模型构建无溢出效应的（非尖锐）原假设分布。我们证明该方法在温和假设下具有精确的有限样本有效性。该方法相比其他方法展现出更强的检验功效，在复杂实验设计中具有显著改进优势。特别需要指出的是，该方法可简化为简单置换检验，便于实际应用。我们通过模拟研究验证了方法的有限样本有效性，并以中国农村天气保险采纳实验为例说明如何运用该方法检验干扰效应。