Graph Neural Networks for Causal Inference Under Network Confounding
Prof. Michael Pak shing LEUNG
Associate Professor,
Economics Department,
University of California, Santa Cruz
Date: 23 June 2025 (Monday)
Time: 10:00-11:30
Venue: E22-G015
Host: Prof. Yi DING, Assistant Professor of Business Economics
Abstract
This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in potential outcomes and selection into treatment. Both stages may be the outcomes of simultaneous equation models, which allow for endogenous peer effects. This results in high-dimensional network confounding where the network and covariates of all units constitute sources of selection bias. In contrast, the existing literature assumes that confounding can be summarized by a known, low-dimensional function of these objects. We propose to use graph neural networks (GNNs) to adjust for network confounding. When interference decays with network distance, we argue that the model has low-dimensional structure that makes estimation feasible and justifies the use of shallow GNN architectures.
Speaker
Prof. Michael Leung is an Associate Professor at the University of California, Santa Cruz. His area of research is econometric theory with a focus on causal inference and methods for analyzing network and spatial data.
All are welcome!
