New Estimands for Experiments with Strong Interference

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 that assume only the randomization of treatment. However, the causal implications of these estimands are more limited than those attainable under stronger assumptions. The estimand shows whether the treatment effects under the observed assignment varied systematically as a function of each unit’s direct and indirect exposure to treatment, while also lower bounding the number of units affected. Supplementary materials for this article are available online.

在研究同伴影响、群体免疫等社会现象的实验中，某一实验单元（unit）所接受的干预（treatment）可能会对其他单元的结局产生影响。这类“单元间干扰”违背了传统因果推断（causal inference）方法的适用前提，因此通常需要引入额外假设以建模或约束潜在的社会作用机制。针对二分类结局变量，本文提出了无需此类假设即可开展估计的新型估计量（estimand），可得到仅依赖干预随机化假设的区间估计。不过，此类估计量所能推导出的因果结论，相较于更强假设下可获得的推论更为有限。该估计量可反映观测分配机制下的干预效应是否随各单元直接、间接接受的干预暴露水平呈系统性变化，同时还能对受影响单元的数量给出下界估计。本文的补充材料可在线获取。