Estimating Peer Effects Using Partial Network Data

2025-09-09

AI Summary

This paper addresses the core challenge in estimating peer effects in social networks—unobserved or mismeasured network structure due to sampling bias, link censoring, or misclassification. Methodologically, it proposes a novel identification strategy that does not require full network data, building instead on a linear mean model framework wherein consistent estimation of the degree distribution suffices for unbiased peer effect identification. This approach mitigates the downward bias inherent in conventional methods that ignore network measurement error. Empirically, using the Add Health dataset, the study demonstrates that correcting for network errors substantially increases estimated peer effects in students’ academic achievement, confirming that neglecting data imperfections systematically underestimates true causal impacts. By relaxing the strong requirement of precise network topology, the paper advances robust and feasible econometric inference for peer effects under partial network observability.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students' academic achievement using the widely used Add Health database, and show that network data errors have a large downward bias on estimated peer effects.

Problem

• Estimating peer effects with incomplete network data
• Addressing sampled, censored, and misclassified network links
• Correcting downward bias in peer effect estimates

Innovation

• Estimating peer effects with partial network data
• Using linear-in-means model with consistent estimator
• Applying method to Add Health academic achievement study