发表机构
Adelaide University; Hainan University（阿德莱德大学; 海南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SiDE估计器，在符号网络中区分正负关系的同伴效应，并通过半合成实验和学校数据验证，揭示符号盲分配会混合影响，为网络干预设计提供指导。
AI 中文摘要
评估网络干预需要理解处理如何通过社会关系影响人们。仅计算被处理的邻居数量而不区分支持性关系与敌对关系，可能会掩盖相反的影响。我们定义了通过正负关系、其交互作用以及在固定总量下在两种关系类型之间重新分配处理的符号组合效应，并给出了它们的识别公式。在符号盲分配下，我们展示了忽略符号如何混合两种关系类型的影响。我们提出了SiDE（符号暴露双重稳健估计器），它将符号特定的结果模型与个体处理分配诱导的暴露概率相结合。我们建立了其得分的双重稳健性，并评估了考虑重叠邻域的近似区间。在六个真实符号网络上的半合成实验展示了准确的效果估计，并检验了区间覆盖的极限。对已发表的学校实验数据的探索性重新分析得出，通过共度时间关系对手环佩戴的同伴效应为正估计，但在多重比较调整后，所有四种效应的区间均包含零。该框架通过展示两种关系类型的影响何时相互增强或抵消，可为网络干预设计提供信息。
英文摘要
Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their interaction, and a sign-composition effect of reallocating treatment between the two types at a fixed total, and give their identification formulas. Under sign-blind assignment, we show how ignoring signs mixes the effects of the two tie types. We propose SiDE (Signed-exposure Doubly robust Estimator), which combines sign-specific outcome models with exposure probabilities induced by individual treatment assignment. We establish double robustness of its score and assess approximate intervals that account for overlapping neighborhoods. Semi-synthetic experiments on six real signed networks demonstrate accurate effect estimation and examine the limits of interval coverage. An exploratory reanalysis of published school-experiment data yields a positive estimate of the peer effect through spend-time ties on wristband wearing, but the intervals for all four effects include zero after adjustment for multiple comparisons. This framework can inform network intervention design by showing when influences through the two tie types reinforce or offset one another.
Comments12 pages
