AI 中文总结
针对群体互动实验，提出基于设计的推断方法，覆盖固定/随机分组及有无干扰场景，证明聚类稳健推断的一致性，并引入新耦合策略。
AI 中文摘要
一种常见的实验研究设计是将个体随机分配到不同群体，然后这些群体在不同的群体层面处理条件下进行互动。我们为这种“群体互动”实验开发了基于设计的推断，涵盖了群体固定或随机形成的情况，以及潜在结果相对于他人的群体分配是固定的或受到干扰的情况。对于每种情况，我们描述了设计所针对的因果估计量以及适合它的推断策略。在稀疏抽样渐近框架下，我们证明当存在干扰时，聚类稳健推断保持一致性并解释了来自各种来源的依赖关系，从而对边际暴露效应提供有效推断。当不存在干扰且群体随机形成时，该设计简化为个体随机实验，并且个体层面的异方差稳健推断足以用于平均处理效应。我们关于常用估计量渐近分布的结果依赖于一种新颖的耦合策略，该策略可能对其他复杂实验中的基于设计推断有用。
英文摘要
A common experimental research design is one in which individuals are randomly allocated into groups that then interact under different group-level treatment conditions. We develop design-based inference for such "group interaction" experiments, covering scenarios in which groups are either fixed or randomly formed and in which potential outcomes are either fixed relative to others' group assignments or subject to interference. For each scenario, we characterize the causal estimand that the design targets and the inferential strategy appropriate to it. Working in a sparse-sampling asymptotic regime, we show that cluster-robust inference remains consistent and accounts for dependencies from various sources when interference is present, delivering valid inference on marginalized exposure effects. When interference is absent and groups are formed randomly, the design reduces to an individually randomized experiment, and individual-level heteroskedasticity-robust inference suffices for the average treatment effect. Our results on the asymptotic distribution of commonly used estimators rely on a novel coupling strategy that may be useful for design-based inference in other complex experiments.
