# Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects

arXiv: https://arxiv.org/abs/2403.11332 · HTML: https://mlsanity.com/paper/2403.11332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 69 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 30 of 31)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Learning Individual Causal Effects from Networked Observational Data](https://mlsanity.com/paper/1906.03485/) (2019)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Causal Inference under Networked Interference and Intervention Policy Enhancement](https://mlsanity.com/paper/2002.08506/) (2020)
- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Using Embeddings to Correct for Unobserved Confounding in Networks](https://mlsanity.com/paper/1902.04114/) (2019)
- [Causal Inference Under Interference And Network Uncertainty](https://mlsanity.com/paper/1907.00221/) (2019)
- [Causal Diagrams for Interference](https://mlsanity.com/paper/1403.1239/) (2014)
- [Bipartite Causal Inference with Interference](https://mlsanity.com/paper/1807.08660/) (2018)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [MALTS: Matching After Learning to Stretch](https://mlsanity.com/paper/1811.07415/) (2018)
- [Causal Relational Learning](https://mlsanity.com/paper/2004.03644/) (2020)
- [Machine learning for causal inference: on the use of cross-fit estimators](https://mlsanity.com/paper/2004.10337/) (2020)
- [Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment](https://mlsanity.com/paper/1305.6156/) (2013)
- [Average Direct and Indirect Causal Effects under Interference](https://mlsanity.com/paper/2104.03802/) (2021)
- [Using Embeddings for Causal Estimation of Peer Influence in Social Networks](https://mlsanity.com/paper/2205.08033/) (2022)
- [Spatial causal inference in the presence of unmeasured confounding and interference](https://mlsanity.com/paper/2303.08218/) (2023)
- [Testing for arbitrary interference on experimentation platforms](https://mlsanity.com/paper/1704.01190/) (2017)
- [Network experimentation at scale](https://mlsanity.com/paper/2012.08591/) (2020)
- [The Local Approach to Causal Inference under Network Interference](https://mlsanity.com/paper/2105.03810/) (2021)
- [Heterogeneous Treatment Effects in Social Networks](https://mlsanity.com/paper/2105.10591/) (2021)

## Built on by (most cited first, 4 of 4)

- [Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects](https://mlsanity.com/paper/2503.01722/) (2025)
- [Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning](https://mlsanity.com/paper/2509.18484/) (2025)
- [The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inference](https://mlsanity.com/paper/2511.13018/) (2025)
- [When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects](https://mlsanity.com/paper/2608.27187/) (2026)

## Abstract (arXiv)

We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is extensive literature focusing on estimating causal effects in social network setups, a majority of them make prior assumptions about the form of network-induced confounding mechanisms. Such strong assumptions are rarely likely to hold especially in high-dimensional networks. We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework to enable accurate and efficient estimation of direct and peer effects using a single observational social network. We demonstrate the semiparametric efficiency of our proposed estimator under mild regularity conditions, allowing for consistent uncertainty quantification. We demonstrate that our method is accurate, robust, and scalable via an extensive simulation study. We use our method to investigate the impact of Self-Help Group participation on financial risk tolerance.