  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.
