  Interference is a ubiquitous problem in experiments conducted on two-sided
content marketplaces, such as Douyin (China's analog of TikTok). In many cases,
creators are the natural unit of experimentation, but creators interfere with
each other through competition for viewers' limited time and attention. "Naive"
estimators currently used in practice simply ignore the interference, but in
doing so incur bias on the order of the treatment effect. We formalize the
problem of inference in such experiments as one of policy evaluation.
Off-policy estimators, while unbiased, are impractically high variance. We
introduce a novel Monte-Carlo estimator, based on "Differences-in-Qs" (DQ)
techniques, which achieves bias that is second-order in the treatment effect,
while remaining sample-efficient to estimate. On the theoretical side, our
contribution is to develop a generalized theory of Taylor expansions for policy
evaluation, which extends DQ theory to all major MDP formulations. On the
practical side, we implement our estimator on Douyin's experimentation
platform, and in the process develop DQ into a truly "plug-and-play" estimator
for interference in real-world settings: one which provides robust, low-bias,
low-variance treatment effect estimates; admits computationally cheap,
asymptotically exact uncertainty quantification; and reduces MSE by 99\%
compared to the best existing alternatives in our applications.
