Increasingly, there is a marked interest in estimating causal effects under network interference due to the fact that interference manifests naturally in networked experiments. However, network information generally is available only up to some level of error. We study the propagation of such errors to estimators of average causal effects under network interference. Specifically, assuming a four-level exposure model and Bernoulli random assignment of treatment, we characterize the impact of network noise on the bias and variance of standard estimators in homogeneous and inhomogeneous networks. In addition, we propose method-of-moments estimators for bias reduction where a minimal number of network replicates are available. We show our estimators are asymptotically normal and provide confidence intervals for quantifying the uncertainty in these estimates. We illustrate the practical performance of our estimators through simulation studies in British secondary school contact networks.
翻译:由于网络实验中干扰自然出现干扰,因此人们越来越有兴趣估计网络干扰造成的因果关系。但是,网络信息一般只有某种程度的错误。我们研究在网络干扰下向平均因果关系估计者传播这种错误的情况。具体地说,假设一种四级接触模型和伯努利随机分配治疗方法,我们就网络噪音对同质和不相容网络中标准估计者的偏差和差异的影响进行定性。此外,我们提出在网络复制数量极少的情况下减少偏差的衡量方法。我们向估计者展示我们的估计是零星正常的,并提供信心间隔,以量化这些估计的不确定性。我们通过在英国中学联系网络的模拟研究来说明我们的估计者的实际表现。