Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity by Yike Wang

Join us for this week's Econometrics Research Seminar, Summer Term 2025

Yike Wang, from the London School of Economics and Political Science will present this week's seminar on Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity

Abstract

In this talk, we present a novel application of graph neural networks for modeling and estimating network heterogeneity. Network heterogeneity is a concept characterizing the dependence of an individual’s outcome or decision on their diverse local network scenarios. Graph neural networks are powerful tools for studying this dependence. We delineate the convergence rate of the graph neural networks estimator, as well as its applicability in semiparametric causal inference with heterogeneous treatment effects. The finite-sample performance of our estimator is evaluated through Monte Carlo simulations. In an empirical setting related to microfinance program participation, we apply the new estimator to examine the average treatment effects and outcomes of counterfactual policies, and to propose a Pareto frontier of strategies for selecting the initial recipients of program information in social networks.

This seminar will be held on campus, is open to all levels of study and is also open to the public. To register your place and gain access to the webinar, please contact the seminar organisers.

This event is part of the Econometrics Research Seminar Series.