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 new estimands that can be estimated without such assumptions, allowing for interval estimates assuming only the randomization of treatment. However, the causal implications of these estimands are more limited than those attainable under stronger assumptions, showing only that the treatment effects under the observed assignment varied systematically as a function of each unit's direct and indirect exposure, while also lower bounding the number of units affected.

翻译：在研究社会现象（如同伴影响或群体免疫）的实验中，一个单元的处理可能会影响其他单元的结果。这种"单元间干扰"违反了传统的因果推断方法，因此通常需要施加额外假设来建模或限制潜在的社会机制。针对二元结果，我们提出了无需此类假设即可估计的新估计目标，仅需假设处理的随机化即可获得区间估计。然而，这些估计目标的因果含义比在更强假设下可获得的结论更为有限，仅能表明观察到的分配下处理效应随每个单元的直接和间接暴露程度而系统变化，同时还能为受影响单元的数量提供一个下限。