We give an approach for characterizing interference by lower bounding the number of units whose outcome depends on certain groups of treated individuals, such as depending on the treatment of others, or others who are at least a certain distance away. The approach is applicable to randomized experiments with binary-valued outcomes. Asymptotically conservative point estimates and one-sided confidence intervals may be constructed with no assumptions beyond the known randomization design, allowing the approach to be used when interference is poorly understood, or when an observed network might only be a crude proxy for the underlying social mechanisms. Point estimates are equal to Hajek-weighted comparisons of units with differing levels of treatment exposure. Empirically, we find that the size of our interval estimates is competitive with (and often smaller than) those of the EATE, an assumption-lean treatment effect, suggesting that the proposed estimands may be intrinsically easier to estimate than treatment effects.

翻译：本文提出一种用于表征干扰的方法，该方法通过下界估计结果依赖于特定处理组（例如依赖于他人处理状态，或至少与特定距离外个体处理状态相关）的单元数量。该方法适用于具有二值化结果的随机化实验。仅基于已知随机化设计即可构建渐近保守的点估计与单侧置信区间，无需额外假设，这使得本方法在干扰机制尚不明确、或观测网络仅能粗略反映潜在社会机制的场景中具有适用性。点估计值等价于对不同处理暴露水平单元进行Hajek加权比较的结果。实证研究表明，本方法所得区间估计的规模与EATE（一种假设宽松的处理效应）的估计区间具有竞争力（且通常更小），这表明所提出的估计量可能本质上比处理效应更容易估计。