Joint work with Vít Illichmann. DRAFT COMING SOON! Python package available here.

Abstract. Social and economic interactions on networks are inherently complex, and econometric methods offer only a limited toolkit for empirically studying highly nonlinear theories of social behavior, such as learning models. To address this challenge, we bridge Graph Neural Networks with the adversarial framework for structural econometric estimation by Kaji, Manresa, and Pouliot (2023). Specifically, we adapt adversarial estimation to graph-structured data by integrating geometric deep learning into the discriminator and introducing a surrogate model for the outer minimization problem. We demonstrate the feasibility of our approach on the canonical linear-in-means model of peer effects, and provide an open-source Python implementation available as a lightweight prototype on GitHub and PyPI.