Causal inference for social network formation

TL;DR

This paper introduces a new framework for identifying and estimating causal mechanisms in social network formation, addressing challenges like unobserved confounders and reverse causality using repeated observations and random variation.

Contribution

It presents a design-based approach that leverages repeated network data and random initial ties to identify causal effects in social networks, bypassing sampling and asymptotic issues.

Findings

Indirect ties significantly increase the likelihood of tie formation.

Effects of network degree and density are smaller and less consistent.

The approach is applied to data from a professional services firm.

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.