Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations
| Autor: | Cosma Rohilla Shalizi, Edward McFowland |
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| Rok vydání: | 2021 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Statistics and Probability Physics - Physics and Society Computer science Node (networking) FOS: Physical sciences Computer Science - Social and Information Networks Physics and Society (physics.soc-ph) 01 natural sciences Data science Homophily Methodology (stat.ME) 010104 statistics & probability Causal inference 0103 physical sciences Peer influence Observational study 0101 mathematics Statistics Probability and Uncertainty 010306 general physics Statistics - Methodology Social influence |
| Zdroj: | Journal of the American Statistical Association. 118:707-718 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1080/01621459.2021.1953506 |
| Popis: | Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, i.e., 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 which directly reflects how much information the network provides about the latent attributes. These are the first results on the consistent non-experimental estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them. Comment: 35 pages, 4 figures |
| Databáze: | OpenAIRE |
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