  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.
