Estimating the effects of interventions in networks is complicated when the
units are interacting, such that the outcomes for one unit may depend on the
treatment assignment and behavior of many or all other units (i.e., there is
interference). When most or all units are in a single connected component, it
is impossible to directly experimentally compare outcomes under two or more
global treatment assignments since the network can only be observed under a
single assignment. Familiar formalism, experimental designs, and analysis
methods assume the absence of these interactions, and result in biased
estimators of causal effects of interest. While some assumptions can lead to
unbiased estimators, these assumptions are generally unrealistic, and we focus
this work on realistic assumptions. Thus, in this work, we evaluate methods for
designing and analyzing randomized experiments that aim to reduce this bias and
thereby reduce overall error. In design, we consider the ability to perform
random assignment to treatments that is correlated in the network, such as
through graph cluster randomization. In analysis, we consider incorporating
information about the treatment assignment of network neighbors. We prove
sufficient conditions for bias reduction through both design and analysis in
the presence of potentially global interference. Through simulations of the
entire process of experimentation in networks, we measure the performance of
these methods under varied network structure and varied social behaviors,
finding substantial bias and error reductions. These improvements are largest
for networks with more clustering and data generating processes with both
stronger direct effects of the treatment and stronger interactions between
units.

评估减少网络干扰的随机实验设计和分析方法，通过仿真实验证明这些方法能够在各种网络结构和社会行为中显著地减少偏差和误差，特别是对于网络中存在更多聚类的情况和数据生成过程中存在更强的治疗直接效应和单位之间更强的相互作用的情况。