A/B tests are often required to be conducted on subjects that might have
social connections. For e.g., experiments on social media, or medical and
social interventions to control the spread of an epidemic. In such settings,
the SUTVA assumption for randomized-controlled trials is violated due to
network interference, or spill-over effects, as treatments to group A can
potentially also affect the control group B. When the underlying social network
is known exactly, prior works have demonstrated how to conduct A/B tests
adequately to estimate the global average treatment effect (GATE). However, in
practice, it is often impossible to obtain knowledge about the exact underlying
network. In this paper, we present UNITE: a novel estimator that relax this
assumption and can identify GATE while only relying on knowledge of the
superset of neighbors for any subject in the graph. Through theoretical
analysis and extensive experiments, we show that the proposed approach performs
better in comparison to standard estimators.

本研究提出了一种名为 UNITE 的新估计方法，可以在不知道具体底层网络的情况下，仅依赖于图中任意主体的邻居超集的信息，来识别全局平均治疗效应（GATE）。通过理论分析和大量实验证明，所提出的方法在比较中表现更好。

具有部分网络信息的干扰下的 A/B 测试

A/B testing under Interference with Partial Network Information

Switchback experimental design, wherein a single unit (e.g., a whole system)
is exposed to a single random treatment for interspersed blocks of time,
tackles both cross-unit and temporal interference. Hu and Wager (2022) recently
proposed a treatment-effect estimator that truncates the beginnings of blocks
and established a $T^{-1/3}$ rate for estimating the global average treatment
effect (GATE) in a Markov setting with rapid mixing. They claim this rate is
optimal and suggest focusing instead on a different (and design-dependent)
estimand so as to enjoy a faster rate. For the same design we propose an
alternative estimator that uses the whole block and surprisingly show that it
in fact achieves an estimation rate of $\sqrt{\log T/T}$ for the original
design-independent GATE estimand under the same assumptions.

用整个块来进行估计，我们提出了一种替代性的估计器，令人惊讶的是，它在相同的假设下实际上实现了对原始独立于设计的全局平均治疗效应的估计率为 sqrt (log T/T) 的速度。