Causal inference for social network formation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914491161116672 |
|---|---|
| author | Kasy, Maximilian Linos, Elizabeth Mobasseri, Sanaz |
| author_facet | Kasy, Maximilian Linos, Elizabeth Mobasseri, Sanaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17952 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal inference for social network formation Kasy, Maximilian Linos, Elizabeth Mobasseri, Sanaz Econometrics Social and Information Networks Applications 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. |
| title | Causal inference for social network formation |
| topic | Econometrics Social and Information Networks Applications |
| url | https://arxiv.org/abs/2604.17952 |