# Neighborhood Adaptive Estimators for Causal Inference under Network Interference

arXiv: https://arxiv.org/abs/2212.03683 · HTML: https://mlsanity.com/paper/2212.03683/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Balancing Covariates via Propensity Score Weighting](https://mlsanity.com/paper/1609.07494/) (2016)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests](https://mlsanity.com/paper/2010.09911/) (2020)
- [Causal inference with misspecified exposure mappings: separating definitions and assumptions](https://mlsanity.com/paper/2103.06471/) (2021)
- [Elements of estimation theory for causal effects in the presence of network interference](https://mlsanity.com/paper/1702.03578/) (2017)
- [Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations](https://mlsanity.com/paper/1607.06565/) (2016)
- [A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference](https://mlsanity.com/paper/1910.10862/) (2019)
- [Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference](https://mlsanity.com/paper/2003.00964/) (2020)
- [Graph Neural Networks for Causal Inference Under Network Confounding](https://mlsanity.com/paper/2211.07823/) (2022)
- [A systematic investigation of classical causal inference strategies under mis-specification due to network interference](https://mlsanity.com/paper/1810.08259/) (2018)
- [Design-Based Inference for Spatial Experiments under Unknown Interference](https://mlsanity.com/paper/2010.13599/) (2020)
- [Limit Theorems for Network Dependent Random Variables](https://mlsanity.com/paper/1903.01059/) (2019)

## Built on by (most cited first, 8 of 8)

- [Higher-Order Causal Message Passing for Experimentation with Complex Interference](https://mlsanity.com/paper/2411.00945/) (2024)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Scalable Policy Maximization Under Network Interference](https://mlsanity.com/paper/2505.18118/) (2025)
- [Model-Based Inference and Experimental Design for Interference Using Partial Network Data](https://mlsanity.com/paper/2406.11940/) (2024)
- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)
- [HNCI: High-Dimensional Network Causal Inference](https://mlsanity.com/paper/2412.18568/) (2024)
- [Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems](https://mlsanity.com/paper/2603.01339/) (2026)
- [Adaptive Policy Learning Under Unknown Network Interference](https://mlsanity.com/paper/2605.11191/) (2026)

## Abstract (arXiv)

Estimating causal effects has become an integral part of most applied fields. In this work we consider the violation of the classical no-interference assumption with units connected by a network. For tractability, we consider a known network that describes how interference may spread. Unlike previous work the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different (local) sub-networks and the assigned treatments. We study estimators for the average direct treatment effect on the treated in such a setting under additive treatment effects. We establish rates of convergence and distributional results. The proposed estimators considers all possible radii for each (local) treatment assignment pattern. In contrast to previous work, we approximate the relevant network interference patterns that lead to good estimates of the interference. To handle feature engineering, a key innovation is to propose the use of synthetic treatments to decouple the dependence. We provide simulations, an empirical illustration and insights for the general study of interference.