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

Estimates of individual treatment effects from networked observational data
are attracting increasing attention these days. One major challenge in network
scenarios is the violation of the stable unit treatment value assumption
(SUTVA), which assumes that the treatment assignment of a unit does not
influence others' outcomes. In network data, due to interference, the outcome
of a unit is influenced not only by its treatment (i.e., direct effects) but
also by others' treatments (i.e., spillover effects). Furthermore, the
influences from other units are always heterogeneous (e.g., friends with
similar interests affect a person differently than friends with different
interests). In this paper, we focus on the problem of estimating individual
treatment effects (both direct and spillover effects) under heterogeneous
interference. To address this issue, we propose a novel Dual Weighting
Regression (DWR) algorithm by simultaneously learning attention weights that
capture the heterogeneous interference and sample weights to eliminate the
complex confounding bias in networks. We formulate the entire learning process
as a bi-level optimization problem. In theory, we present generalization error
bounds for individual treatment effect estimation. Extensive experiments on
four benchmark datasets demonstrate that the proposed DWR algorithm outperforms
state-of-the-art methods for estimating individual treatment effects under
heterogeneous interference.

本论文提出了一种新算法 ——Dual Weighting Regression (DWR)，其通过学习注意力权重和样本权重来解决网络场景下的干扰异质性问题，同时实验表明该算法优于目前的其他算法。