  Policy-relevant treatment effect estimation in a marketplace setting requires
taking into account both the direct benefit of the treatment and any spillovers
induced by changes to the market equilibrium. The standard way to address these
challenges is to evaluate interventions via cluster-randomized experiments,
where each cluster corresponds to an isolated market. This approach, however,
cannot be used when we only have access to a single market (or a small number
of markets). Here, we show how to identify and estimate policy-relevant
treatment effects using a unit-level randomized trial run within a single large
market. A standard Bernoulli-randomized trial allows consistent estimation of
direct effects, and of treatment heterogeneity measures that can be used for
welfare-improving targeting. Estimating spillovers - as well as providing
confidence intervals for the direct effect - requires estimates of price
elasticities, which we provide using an augmented experimental design. Our
results rely on all spillovers being mediated via the (observed) prices of a
finite number of traded goods, and the market power of any single unit decaying
as the market gets large. We illustrate our results using a simulation
calibrated to a conditional cash transfer experiment in the Philippines.
