  It is standard practice in online retail to run pricing experiments by
randomizing at the article-level, i.e. by changing prices of different products
to identify treatment effects. Due to customers' cross-price substitution
behavior, such experiments suffer from interference bias: the observed
difference between treatment groups in the experiment is typically
significantly larger than the global effect that could be expected after a
roll-out decision of the tested pricing policy. We show in simulations that
such bias can be as large as 100%, and report experimental data implying bias
of similar magnitude. Finally, we discuss approaches for de-biased pricing
experiments, suggesting observational methods as a potentially attractive
alternative to clustering.
