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 often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose an approach that does not require such assumptions, allowing for interference that is both unmodeled and strong, with confidence intervals derived using only the randomization of treatment. However, the estimates will have wider confidence intervals and weaker causal implications than those attainable under stronger assumptions. 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 using a vaccination trial and two experiments investigating social influence.

翻译：在研究社会现象的实验中,例如同侪影响或牧群豁免,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”违反了传统的因果关系推断方法,因此往往会强加更多的假设来模拟或限制基本社会机制。关于二进制结果,我们提议一种不要求这种假设的方法,允许未经改造和强大的干涉,只使用治疗随机化方法得出信任间隔,但是,估计会比在较强的假设下可以达到的具有更大的信任间隔和较弱的因果关系。这种方法允许使用回归、匹配或加权,因为可能最适合手头应用。推理方法是将估计错误的分布与未知反事实的所有可能值相挂钩,使用一个整数方案,用接种试验和两个实验来调查社会影响,可以证明这些例子。