A key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. Here, we study this question using a segregated graph (Shpitser, 2015) representation of these mechanisms, and examine how uncertainty about the true underlying mechanism impacts downstream computation of network causal effects, particularly under full interference -- settings where we only have a single realization of a network and each unit may depend on any other unit in the network. Under certain assumptions about asymptotic growth of the network, we derive likelihood ratio tests that can be used to identify whether different sets of variables -- confounders, treatments, and outcomes -- across units exhibit dependence due to contagion or latent confounding. We then propose network causal effect estimation strategies that provide unbiased and consistent estimates if the dependence mechanisms are either known or correctly inferred using our proposed tests. Together, the proposed methods allow network effect estimation in a wider range of full interference scenarios that have not been considered in prior work. We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.

翻译：许多网络研究中的一个核心问题是，观测到的单元间相关性主要源于传染效应还是潜在混杂因素。本文采用分离图（Shpitser, 2015）表示这两种机制，探讨对真实底层机制的不确定性如何影响网络因果效应的后续计算，特别是在完全干扰场景下——即我们仅拥有网络的单次实现且每个单元可能依赖于网络中任意其他单元的情形。在关于网络渐近增长的特定假设下，我们推导出似然比检验方法，可用于判别不同变量集（混杂变量、处理变量与结果变量）在单元间表现出的依赖关系究竟源于传染效应还是潜在混杂。随后，我们提出网络因果效应估计策略，若依赖机制已知或通过我们提出的检验正确推断，该策略可提供无偏且一致的估计量。综合而言，所提出的方法能够在更广泛的完全干扰场景中实现网络效应估计，这类场景在先前研究中尚未被充分考虑。我们通过合成数据评估方法的有效性，并利用真实网络验证假设的合理性。