Multicell Experiments for Marginal Treatment Effect Estimation of Digital Ads
针对数字广告实验中常见的单侧不依从问题，提出一种多单元实验设计与现代估计技术相结合的方法，帮助决策者优化广告触达人数或预算分配，并通过Facebook广告实验模拟验证其优越性。
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 the 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 multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches. This paper was accepted by Jean-Pierre Dubé, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01185 .