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.

本研究解决了网络研究中观察到的单位间相关性究竟是源于传染效应还是潜在混淆的问题。通过采用分离图的分析方法，我们提出了一种新的网络因果效应估计策略，能够在完全干扰的情况下，为未知或正确推断的依赖机制提供无偏和一致的估计。这项工作的显著发现是，方法在网络效应估计中适用的场景范围比以往研究广泛，具有重要的实际应用潜力。

图形模型中的网络因果效应估计：传染与潜在混淆