  Experiments on online marketplaces and social networks suffer from
interference, where the outcome of a unit is impacted by the treatment status
of other units. We propose a framework for modeling interference using a
ubiquitous deployment mechanism for experiments, staggered roll-out designs,
which slowly increase the fraction of units exposed to the treatment to
mitigate any unanticipated adverse side effects. Our main idea is to leverage
the temporal variations in treatment assignments introduced by roll-outs to
model the interference structure. Since there are often multiple competing
models of interference in practice we first develop a model selection method
that evaluates models based on their ability to explain outcome variation
observed along the roll-out. Through simulations, we show that our heuristic
model selection method, Leave-One-Period-Out, outperforms other baselines.
Next, we present a set of model identification conditions under which the
estimation of common estimands is possible and show how these conditions are
aided by roll-out designs. We conclude with a set of considerations, robustness
checks, and potential limitations for practitioners wishing to use our
framework.
