Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties

2026-10-02

AI Summary

This study addresses a critical limitation in network intervention research, where the frequent neglect of positive and negative signs in social ties causes opposing causal influences to become confounded. To resolve this, the work formally defines signed tie effects and their interactions for the first time, deriving corresponding identification formulas. It further proposes SiDE, a doubly robust estimator that integrates sign-specific outcome models with exposure probabilities to disentangle heterogeneous causal effects. Experiments conducted across six real-world signed networks demonstrate that SiDE achieves accurate causal estimation and valid interval coverage. Moreover, the method reveals underlying cancellation and amplification mechanisms between positive and negative influences. By rigorously separating these opposing forces, this research establishes a novel paradigm for optimizing the design of network interventions.

Abstract

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.

Problem

• peer effects
• signed networks
• network intervention
• causal inference

Innovation

• Signed Networks
• Peer Effects
• Doubly Robust Estimator
• Causal Inference
• Network Interventions