Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • 2023
  • Article
  • Journal of the American Statistical Association

Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.

By: Edward McFowland III and Cosma Rohilla Shalizi
  • Format:Print
  • | Pages:12
ShareBar

Abstract

Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its behavior. If the network grows according to either a latent community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. We show that, for common versions of those two network models, these estimates are so informative that controlling for estimated attributes allows for asymptotically unbiased and consistent estimation of social-influence effects in linear models. In particular, the bias shrinks at a rate that directly reflects how much information the network provides about the latent attributes. These are the first results on the consistent nonexperimental estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them.

Keywords

Causal Inference; Homophily; Social Networks; Peer Influence; Social and Collaborative Networks; Power and Influence; Mathematical Methods

Citation

McFowland III, Edward, and Cosma Rohilla Shalizi. "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations." Journal of the American Statistical Association 118, no. 541 (2023): 707–718.
  • Find it at Harvard
  • Purchase

About The Author

Edward McFowland III

Technology and Operations Management
→More Publications

More from the Authors

    • 2023
    • Journal of Machine Learning Research

    Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

    By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
    • October–December 2022
    • INFORMS Journal on Data Science

    Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

    By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
    • 2022
    • Journal of Computational and Graphical Statistics

    Nonparametric Subset Scanning for Detection of Heteroscedasticity

    By: Charles R. Doss and Edward McFowland III
More from the Authors
  • Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
  • Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
  • Nonparametric Subset Scanning for Detection of Heteroscedasticity By: Charles R. Doss and Edward McFowland III
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College