Network interference occurs when the treatment status of one unit affects the potential outcomes of other units, giving rise to spillover effects that are difficult to test for. We propose treating the network as a random variable rather than a fixed quantity to address this challenge. This overcomes a key challenge of non-imputability of potential outcomes under the null and avoids the computational intractability of existing conditional randomization tests. Our quasi-randomization test builds the null distribution of no spillover effects using random graph null models, is exactly valid in finite samples under mild assumptions on the network-generating process, and offers substantially improved power over existing methods, particularly in cluster-randomized trials. We validate our approach via simulation and illustrate it by testing for interference in a weather insurance adoption experiment in rural China.

翻译：摘要：当一个体的处理状态影响其他个体的潜在结果时，即产生网络干扰，导致难以检验的溢出效应。针对这一挑战，我们提出将网络视为随机变量而非固定量。这克服了原假设下潜在结果不可归因的关键难题，并避免了现有条件随机化检验在计算上的难解性。我们的拟随机化检验利用随机图零模型构建无溢出效应的原假设分布，在关于网络生成过程的温和假设下，对有限样本具有精确有效性，并显著提升了现有方法的检验功效——在整群随机试验中尤为突出。我们通过模拟验证了该方法，并利用中国农村天气保险采纳实验中的干扰检验进行了实例说明。