Interference occurs when the potential outcomes of a unit depend on the treatments assigned to other units. That is frequently the case in many domains, such as in the social sciences and infectious disease epidemiology. Often, the interference structure is represented by a network, which is typically assumed to be given and accurate. However, correctly specifying the network can be challenging, as edges can be censored, the structure can change over time, and contamination between clusters may exist. Building on the exposure mapping framework, we derive the bias arising from estimating causal effects under a misspecified interference structure. To address this problem, we propose a novel estimator that uses multiple networks simultaneously and is unbiased if one of the networks correctly represents the interference structure, thus providing robustness to the network specification. Additionally, we propose a sensitivity analysis that quantifies the impact of a postulated misspecification mechanism on the causal estimates. Through simulation studies, we illustrate the bias from assuming an incorrect network and show the bias-variance tradeoff of our proposed network-misspecification-robust estimator. We demonstrate the utility of our methods in two real examples.
翻译:当一个单位的潜在结果取决于分配给其他单位的治疗方法时,就会发生干扰。这往往是许多领域,例如社会科学和传染病流行病学领域经常出现的情况。干预结构通常由网络代表,通常假定网络是给人和准确的。然而,正确指定网络可能是具有挑战性的,因为边缘可以审查,结构会随着时间的推移而变化,而且集群之间的污染可能存在。根据暴露绘图框架,我们从估计错误的干扰结构下的因果关系中得出偏差。为了解决这个问题,我们建议建立一个新的估计器,如果一个网络正确代表了干扰结构,那么同时使用多个网络,而且这种估计器是不偏不倚的。此外,我们提出敏感度分析,以量度测定一个假定的错误区分机制对因果关系估计的影响。我们通过模拟研究,说明假设错误的网络的偏差,并表明我们提议的网络偏差-紫外线估测仪的偏差。我们用两个真实的例子展示了我们方法的效用。