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Area of Science:

  • Psychology
  • Causal Inference
  • Network Analysis

Background:

  • Traditional methods for studying social influence are limited to predefined groups, restricting analysis to within-group interactions.
  • Existing approaches fail to capture social influence in complex social networks where individuals interact across arbitrary connections.
  • Assessing social influence in general social networks requires new estimation methods beyond traditional multilevel models.

Purpose of the Study:

  • To introduce novel methods for assessing social influence in social networks within randomized experiments.
  • To extend the potential outcomes framework to address treatment interference in network settings.
  • To provide valid estimation and inference for social influence in complex social networks.

Main Methods:

  • Developed new randomization-based estimation methods rooted in causal inference principles.
  • Exploited the concept of treatment interference to model cross-individual effects.
  • Employed Monte Carlo simulation studies to compare proposed methods with standard approaches.

Main Results:

  • Proposed methods provide valid inference for social influence in social networks.
  • Simulation studies demonstrated the effectiveness of the new estimation techniques.
  • Empirical illustration using student peer networks showed practical application of the methods.

Conclusions:

  • The introduced methods offer a robust framework for studying social influence in general social networks.
  • These advancements enable more accurate estimation of treatment effects in the presence of network interference.
  • The freely available R scripts facilitate the application of these methods in future research.