This paper studies the measurement of advertising effects on online platforms when parallel experimentation occurs, that is, when multiple advertisers experiment concurrently. It provides a framework that makes precise how parallel experimentation affects the experiment's value: while ignoring parallel experimentation yields an estimate of the average effect of advertising in-place, which has limited value in decision-making in an environment with variable advertising competition, accounting for parallel experimentation captures the actual uncertainty advertisers face due to competitive actions. It then implements an experimental design that enables the estimation of these effects on JD.com, a large e-commerce platform that is also a publisher of digital ads. Using traditional and kernel-based estimators, it shows that not accounting for competitive actions can result in the advertiser inaccurately estimating the advertising lift by a factor of two or higher, which can be consequential for decision-making.

翻译：本文研究了在线平台上发生并行实验（即多个广告主同时进行实验）时广告效果的测量问题。我们提出了一个框架，精确阐述了并行实验如何影响实验价值：忽略并行实验仅能估计广告在现状环境下的平均效果，这在一个广告竞争动态变化的环境中对于决策制定的价值有限；而考虑并行实验则能捕获广告主因竞争行为所面临的实际不确定性。随后，我们在京东（一个同时作为数字广告发布商的大型电商平台）上实施了一种实验设计，使这些效应的估计成为可能。通过使用传统估计量和基于核的估计量，研究表明忽略竞争行为可能导致广告主对广告提升效果的估算出现两倍甚至更高的偏差，这可能对决策产生重大影响。