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Randomized experiments with treatment and control groups are an importanttool to measure the impacts of interventions. However, in experimental settingswith one-sided noncompliance, extant empirical approaches may not produce theestimands a decision-maker needs to solve their problem of interest. Forexample, 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 withmodern estimation techniques that enables decision-makers to recover enoughinformation to solve problems with an intensive margin. Our design isstraightforward to implement and does not require any additional budget to becarried out. We illustrate our approach through a series of simulations thatare calibrated using an advertising experiment at Facebook, finding that ourmethod outperforms standard techniques in generating better decisions.

Multi-cell experiments for marginal treatment effect estimation of digital ads