Social Interactions Models with Latent Structures

2026-02-06

AI Summary

This study investigates heterogeneous peer effects in a binary choice setting where latent group structures induce incidental parameter bias due to group fixed effects and slope heterogeneity. To address this challenge, the paper proposes a novel approach that integrates Classifier-Lasso with a parametric bootstrap procedure: the former automatically detects the latent clustering structure and selects the number of groups, while the latter debiases the estimators to enable valid inference. Theoretical analysis establishes desirable asymptotic properties of the proposed method, and Monte Carlo simulations demonstrate its superior finite-sample performance. Applying the framework to adolescent risk behavior data, the empirical analysis successfully identifies two latent subpopulations and reveals significant peer effects within one of them.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and determine the number of clusters. To solve the incidental parameter problem in the binary choice model with social interactions, we propose a parametric bootstrap method to debias and establish its asymptotic validity. Monte Carlo simulations confirm strong finite sample performance of our methods. In an application to students'risky behaviors, the algorithm detects two latent clusters and finds that peer effects are significant within one of the clusters, demonstrating the practical applicability in uncovering heterogeneous social interactions.

Problem

• peer effects
• latent structure
• incidental parameter problem
• heterogeneous social interactions
• binary choice model

Innovation

• Classifier-Lasso
• latent structure
• peer effects
• incidental parameter problem
• parametric bootstrap