  Online experiments such as Randomised Controlled Trials (RCTs) or A/B-tests
are the bread and butter of modern platforms on the web. They are conducted
continuously to allow platforms to estimate the causal effect of replacing
system variant "A" with variant "B", on some metric of interest. These variants
can differ in many aspects. In this paper, we focus on the common use-case
where they correspond to machine learning models. The online experiment then
serves as the final arbiter to decide which model is superior, and should thus
be shipped.
  The statistical literature on causal effect estimation from RCTs has a
substantial history, which contributes deservedly to the level of trust
researchers and practitioners have in this "gold standard" of evaluation
practices. Nevertheless, in the particular case of machine learning
experiments, we remark that certain critical issues remain. Specifically, the
assumptions that are required to ascertain that A/B-tests yield unbiased
estimates of the causal effect, are seldom met in practical applications. We
argue that, because variants typically learn using pooled data, a lack of model
interference cannot be guaranteed. This undermines the conclusions we can draw
from online experiments with machine learning models. We discuss the
implications this has for practitioners, and for the research literature.
