This paper proposes approximate variational inference methods for estimation of a strategic model of social interactions. Players interact in an exogenous network and sequentially choose a binary action. The utility of an action is a function of the choices of neighbors in the network. I prove...

We develop approximate estimation methods for exponential random graph models (ERGMs), whose likelihood is proportional to an intractable normalizing constant. The usual approach approximates this constant with Monte Carlo simulations, however convergence may be exponentially slow. We propose a...

I present a peer effects model for count data using a static game of incomplete information. I provide sufficient conditions under which the game equilibrium is unique. I estimate the model's parameters using the Nested Partial Likelihood approach and establish asymptotic properties of the...

The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest. In empirical studies, researchers often adopt a two-stage procedure to first estimate the centrality and then infer...

Analyses of treatments, experiments, policies, and observational data, are confounded when people's treatment outcomes and/or participation decisions are influenced by those of their friends and acquaintances. This invalidates standard matching techniques as estimation tools. For instance, the...

Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly...