Multicell Experiments for Marginal Treatment Effect Estimation of Digital Ads

• 成果类型: Article
• 署名作者: Waisman, Caio; Gordon, Brett R.
• 署名单位: Northwestern University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.01185
• 发表日期: 2025
• 关键词: marginal treatment effects; field experiments; Causal Inference; digital advertising; advertising measurement

摘要

Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with onesided 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.