In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such "interference between units" violates traditional approaches for causal inference, so that additional assumptions are required to model the underlying social mechanism. We propose an approach that requires no such assumptions, allowing for interference that is both unmodeled and strong, with confidence intervals found using only the randomization of treatment. Additionally, the approach allows for the usage of regression, matching, or weighting, as may best fit the application at hand. Inference is done by bounding the distribution of the estimation error over all possible values of the unknown counterfactual, using an integer program. Examples are shown using a vaccine trial and two experiments investigating social influence.

翻译：在研究社会现象的实验中,例如同侪影响或群群豁免等,对一个单位的处理可能会影响其他单位的结果。这种“单位之间的干涉”违反了传统的因果推断方法,因此需要额外的假设来模拟基本社会机制。我们建议一种不需要这种假设的方法,允许未经改造的和强大的干预,只使用随机治疗方法,发现信任的间隔。此外,这种方法允许使用回归、匹配或加权,这最适合手头的应用。推论是通过将估计误差与未知反事实的所有可能值相挂钩,使用一个整数方案,通过疫苗试验和两个调查社会影响的实验来进行。