区分网络中传染与潜在混杂的影响，提升因果效应估计准确性
Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding
- 用分离图模型区分传染与混杂机制，明确依赖来源
- 在单次网络观测下，实现无偏一致的因果效应估计
- 适用于复杂依赖场景，适合研究网络传播与干预效果的学者
许多网络研究的核心问题是：单元间的观察相关性主要源于传染还是潜在混杂？本文采用分离图（segregated graph）模型表征这两种机制，分析其不确定性对网络因果效应计算的影响，尤其在全干扰场景下——即仅有一个网络实现实例，且每个单元可能依赖于任意其他单元。在特定网络渐近增长假设下，我们推导出似然比检验方法，可识别不同单元间变量（混杂因子、处理、结果）的依赖是源于传染还是潜在混杂。随后提出因果效应估计策略，当机制已知或通过所提检验正确推断时，可获得无偏且一致的估计。该方法扩展了以往研究在全干扰情形下的适用范围。我们在合成数据上验证方法有效性，并在真实网络中检验假设合理性。
原文摘要 · Abstract (English)
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.
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