Causal inference for social network formation

2026-04-20

AI Summary

This study addresses the challenges of causal inference in the endogenous formation of social networks—specifically unobserved confounding, reverse causality, equilibrium dependence, and sampling bias—by proposing a design-based nonparametric identification framework. Leveraging random variation in initial ties and repeated observations in panel network data, the approach treats nodes and their potential outcomes as non-stochastic, thereby circumventing conventional assumptions of random sampling and asymptotic approximations. An application to professional service firm data reveals a significant positive causal effect of indirect connections on tie formation, whereas the influence of node degree and local density is weak and statistically unstable. These findings underscore the method’s strength in handling the endogeneity and equilibrium complexity inherent in network formation processes.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

This paper develops a framework for identification, estimation, and inference on the causal mechanisms driving endogenous social network formation. Identification is challenging because of unobserved confounders and reverse causality; inference is complicated by questions of equilibrium and sampling. We leverage repeated observations of a network over time and random variation in initial ties to address challenges to causal identification. Our design-based approach sidesteps questions of sampling and asymptotics by treating both the set of nodes (individuals) and potential outcomes as non-random. We apply our approach to data from a large professional services firm, where new hires are randomly assigned to project teams within offices. We estimate the causal effect on tie formation of indirect ties, network degree, and local network density. Indirect ties have a strong and significant positive effect on tie formation, while the effects of degree and density are smaller and less robust.

Problem

• causal inference
• social network formation
• endogenous networks
• unobserved confounders
• reverse causality

Innovation

• causal inference
• social network formation
• design-based inference
• endogenous networks
• random assignment