  Randomized experiments with treatment and control groups are an important
tool to measure the impacts of interventions. However, in experimental settings
with one-sided noncompliance, extant empirical approaches may not produce the
estimands a decision-maker needs to solve their problem of interest. For
example, these experimental designs are common in digital advertising settings,
but typical methods do not yield effects that inform the intensive margin --
how many consumers should be reached or how much should be spent on a campaign.
We propose a solution that combines a novel multi-cell experimental design with
modern estimation techniques that enables decision-makers to recover enough
information to solve problems with an intensive margin. Our design is
straightforward to implement and does not require any additional budget to be
carried out. We illustrate our approach through a series of simulations that
are calibrated using an advertising experiment at Facebook, finding that our
method outperforms standard techniques in generating better decisions.
