# Learning Individual Treatment Effects under Heterogeneous Interference in Networks

arXiv: https://arxiv.org/abs/2210.14080 · HTML: https://mlsanity.com/paper/2210.14080/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [SNAP: A General Purpose Network Analysis and Graph Mining Library](https://mlsanity.com/paper/1606.07550/) (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 Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Attention Models in Graphs: A Survey](https://mlsanity.com/paper/1807.07984/) (2018)
- [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)
- [Learning Causal Effects on Hypergraphs](https://mlsanity.com/paper/2207.04049/) (2022)
- [Causal Inference Under Interference And Network Uncertainty](https://mlsanity.com/paper/1907.00221/) (2019)
- [X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning](https://mlsanity.com/paper/2109.03560/) (2021)
- [Auto-G-Computation of Causal Effects on a Network](https://mlsanity.com/paper/1709.01577/) (2017)
- [Using Embeddings for Causal Estimation of Peer Influence in Social Networks](https://mlsanity.com/paper/2205.08033/) (2022)
- [Generalization bounds and algorithms for estimating conditional average treatment effect of dosage](https://mlsanity.com/paper/2205.14692/) (2022)
- [Generalized propensity score approach to causal inference with spatial interference](https://mlsanity.com/paper/2007.00106/) (2020)
- [Causal inference for multiple continuous exposures via the multivariate generalized propensity score](https://mlsanity.com/paper/2008.13767/) (2020)

## Built on by (most cited first, 12 of 12)

- [Estimating Treatment Effects Under Heterogeneous Interference](https://mlsanity.com/paper/2309.13884/) (2023)
- [Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence](https://mlsanity.com/paper/2305.17479/) (2023)
- [Optimizing Treatment Allocation in the Presence of Interference](https://mlsanity.com/paper/2410.00075/) (2024)
- [Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects](https://mlsanity.com/paper/2503.01722/) (2025)
- [Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption](https://mlsanity.com/paper/2502.19741/) (2025)
- [Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning](https://mlsanity.com/paper/2509.18484/) (2025)
- [Estimating Treatment Effects in Networks under Unknown Exposure Mappings](https://mlsanity.com/paper/2510.21457/) (2025)
- [IntOPE: Off-Policy Evaluation in the Presence of Interference](https://mlsanity.com/paper/2408.13484/) (2024)
- [Qini Curve Estimation under Clustered Network Interference](https://mlsanity.com/paper/2502.20097/) (2025)
- [Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective](https://mlsanity.com/paper/2510.05750/) (2025)
- [Disentangled Instrumental Variables for Causal Inference with Networked Observational Data](https://mlsanity.com/paper/2602.07765/) (2026)
- [Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties](https://mlsanity.com/paper/2610.02872/) (2026)

## Abstract (arXiv)

Estimates of individual treatment effects from networked observational data are attracting increasing attention these days. One major challenge in network scenarios is the violation of the stable unit treatment value assumption (SUTVA), which assumes that the treatment assignment of a unit does not influence others' outcomes. In network data, due to interference, the outcome of a unit is influenced not only by its treatment (i.e., direct effects) but also by others' treatments (i.e., spillover effects). Furthermore, the influences from other units are always heterogeneous (e.g., friends with similar interests affect a person differently than friends with different interests). In this paper, we focus on the problem of estimating individual treatment effects (both direct and spillover effects) under heterogeneous interference. To address this issue, we propose a novel Dual Weighting Regression (DWR) algorithm by simultaneously learning attention weights that capture the heterogeneous interference and sample weights to eliminate the complex confounding bias in networks. We formulate the entire learning process as a bi-level optimization problem. In theory, we present generalization error bounds for individual treatment effect estimation. Extensive experiments on four benchmark datasets demonstrate that the proposed DWR algorithm outperforms state-of-the-art methods for estimating individual treatment effects under heterogeneous interference.