# Graph Neural Networks for Causal Inference Under Network Confounding

arXiv: https://arxiv.org/abs/2211.07823 · HTML: https://mlsanity.com/paper/2211.07823/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2022.

## Built on by (most cited first, 7 of 7)

- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Trading Graph Neural Network](https://mlsanity.com/paper/2504.07923/) (2025)
- [Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interference](https://mlsanity.com/paper/2602.04457/) (2026)
- [Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties](https://mlsanity.com/paper/2610.02872/) (2026)

## Abstract (arXiv)

This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in both potential outcomes and selection into treatment. Specifically, both stages may be the outcomes of simultaneous equations models, allowing 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.