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：当一个个体的潜在结果取决于其他个体所接受的干预时，就会产生干涉效应。这在社会科学和传染病流行病学等众多领域中十分常见。通常，干涉结构由网络表示，并且通常假设该网络是已知且准确的。然而，正确指定网络可能具有挑战性，因为边可能被删失、结构可能随时间变化，且集群之间可能存在污染。基于暴露映射框架，我们推导了在错误指定的干涉结构下估计因果效应所产生的偏倚。为解决这一问题，我们提出了一种新型估计量，该估计量同时使用多个网络，并且只要其中一个网络正确表示了干涉结构，它就是无偏的，从而提供了对网络指定的鲁棒性。此外，我们提出了一种敏感性分析方法，用于量化假定的错误指定机制对因果估计的影响。通过模拟研究，我们说明了假设错误网络所产生的偏倚，并展示了我们提出的网络错误指定鲁棒估计量的偏倚-方差权衡。我们在两个真实案例中展示了我们方法的实用性。