Causal inference for social network formation
Maximilian Kasy, Elizabeth Linos, Sanaz Mobasseri
TLDR
This paper introduces a design-based framework for causal inference in social network formation, using repeated observations and random initial ties to identify key drivers.
Key contributions
- Proposes a design-based framework for causal inference in endogenous social network formation.
- Addresses identification challenges like confounders and reverse causality using repeated observations and random initial ties.
- Applies the framework to a firm, finding indirect ties significantly boost tie formation.
Why it matters
This paper offers a robust, design-based framework to identify causal mechanisms in social network formation, overcoming common challenges like unobserved confounders. It provides a new method for studying network dynamics and offers insights into how indirect ties drive connections, valuable for organizational design.
Original Abstract
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.
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