mlsanityData to 7 Oct 2026

Neighborhood Adaptive Estimators for Causal Inference under Network Interference

arXiv 2212.03683 ↗First posted 7 Dec 2022stat.ML cs.LG econ.EMcited by 8

Not on the map. Why?

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.

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

Builds on

14 of 53 references.

References · 14

  1. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  2. 2016Balancing Covariates via Propensity Score Weightingcited by 65
  3. 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 43
  4. 2014Design and analysis of experiments in networks: Reducing bias from interferencecited by 41
  5. 2020Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Testscited by 14
  6. 2021Causal inference with misspecified exposure mappings: separating definitions and assumptionscited by 14
  7. 2017Elements of estimation theory for causal effects in the presence of network interferencecited by 12
  8. 2016Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locationscited by 8
  9. 2019A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interferencecited by 7
  10. 2020Almost-Matching-Exactly for Treatment Effect Estimation under Network Interferencecited by 7
  11. 2022Graph Neural Networks for Causal Inference Under Network Confoundingcited by 6
  12. 2018A systematic investigation of classical causal inference strategies under mis-specification due to network interferencecited by 4
2 more
  1. 2020Design-Based Inference for Spatial Experiments under Unknown Interferencecited by 2
  2. 2019Limit Theorems for Network Dependent Random Variablescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 3.

8 papers.

  1. 2024Higher-Order Causal Message Passing for Experimentation with Complex Interferencecited by 4
  2. 2023A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testingcited by 3
  3. 2025Scalable Policy Maximization Under Network Interferencecited by 3
  4. 2024Model-Based Inference and Experimental Design for Interference Using Partial Network Datacited by 1
  5. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  6. 2024HNCI: High-Dimensional Network Causal Inferencecited by 0
  7. 2026Causal Effects with Unobserved Unit Types in Interacting Human-AI Systemscited by 0
  8. 2026Adaptive Policy Learning Under Unknown Network Interferencecited by 0