Peer effects differ through positive and negative social ties in networks
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
Machine Learning
Summary
Social connections can be friendly or unfriendly, and these different ties can influence how people are affected by treatments or changes in a group. The authors developed a way to measure how positive and negative relationships separately impact individuals when some people in their network are treated. They created a method that combines different models to estimate these effects accurately, even when the data is complex. Their work helps identify when friends and foes have opposing effects and can guide better design of social interventions.
What this means in practice
- •For public health planners: Design network-based health interventions taking into account both supportive and antagonistic social ties to better predict treatment spread.
- •For organizational managers: Evaluate how positive and negative workplace relationships influence employee behavior changes from new policies or programs within their social networks.
Authors
Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le
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.