The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in this setting are now readily recognized, and partial remedies are well known. Much less understood is the impact of population interference in panel experiments where treatment is sequentially randomized in the population, and the outcomes are observed at each time step. This paper proposes a general framework for studying population interference in panel experiments and presents new finite population estimation and inference results. Our findings suggest that, under mild assumptions, the addition of a temporal dimension to an experiment alleviates some of the challenges of population interference for certain estimands. In contrast, we show that the presence of carryover effects -- that is, when past treatments may affect future outcomes -- exacerbates the problem. Revisiting the special case of standard experiments with population interference, we prove a central limit theorem under weaker conditions than previous results in the literature and highlight the trade-off between flexibility in the design and the interference structure.

翻译：在标准随机化实验中，一个实验单元所接受的处理会影响另一实验单元结果的现象（即总体干扰）已受到广泛关注。这种情境下总体干扰带来的复杂性已被充分认识，部分补救措施也已广为人知。然而，在面板实验（处理在总体中依序随机化，且每个时间步长观测结果）中，总体干扰的影响仍鲜为人知。本文提出了一个研究面板实验中总体干扰的通用框架，并给出了新的有限总体估计与推断结果。研究发现，在温和假设下，为实验增加时间维度可缓解特定估计量面临的总体干扰挑战。相反，我们表明，如果存在结转效应（即过去处理可能影响未来结果），则会加剧该问题。在重新审视具有总体干扰的标准实验这一特例时，我们以弱于现有文献的条件证明了中心极限定理，并揭示了实验设计灵活性与干扰结构之间的权衡关系。