Graph Neural Networks for Causal Inference Under Network Confounding

Michael Leung and Pantelis Loupos

Papers from arXiv.org

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.

Date: 2022-11, Revised 2025-05
New Economics Papers: this item is included in nep-big, nep-ecm and nep-net
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