Experiments 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. For example, these experimental designs are common in
digital advertising settings, but typical methods do not yield effects that
inform the intensive margin -- how much should be spent or how many consumers
should be reached with 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. Using data from
advertising experiments at Facebook, we demonstrate our approach outperforms
standard techniques in recovering treatment effect parameters. Through a simple
advertising reach decision problem, we show that our approach generates better
decisions relative to standard techniques