Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning

• URL: http://arxiv.org/abs/2509.18484v1
• Date: Tue, 23 Sep 2025 00:41:04 GMT
• Title: Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
• Authors: Yuanchen Wu, Yubai Yuan,
• Abstract summary: Estimating causal effects on networks is important for scientific research and practical applications.<n>We propose a two-stage method to estimate heterogeneous direct and spillover effects on networks.
• Score: 8.45884756677776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating causal effects on networks is important for both scientific research and practical applications. Unlike traditional settings that assume the Stable Unit Treatment Value Assumption (SUTVA), interference allows an intervention/treatment on one unit to affect the outcomes of others. Understanding both direct and spillover effects is critical in fields such as epidemiology, political science, and economics. Causal inference on networks faces two main challenges. First, causal effects are typically heterogeneous, varying with unit features and local network structure. Second, connected units often exhibit dependence due to network homophily, creating confounding between structural correlations and causal effects. In this paper, we propose a two-stage method to estimate heterogeneous direct and spillover effects on networks. The first stage uses graph neural networks to estimate nuisance components that depend on the complex network topology. In the second stage, we adjust for network confounding using these estimates and infer causal effects through a novel attention-based interference model. Our approach balances expressiveness and interpretability, enabling downstream tasks such as identifying influential neighborhoods and recovering the sign of spillover effects. We integrate the two stages using Neyman orthogonalization and cross-fitting, which ensures that errors from nuisance estimation contribute only at higher order. As a result, our causal effect estimates are robust to bias and misspecification in modeling causal effects under network dependencies.

Related papers

• On Evolution-Based Models for Experimentation Under Interference [7.262048441360133]
  We study an evolution-based approach that investigates how outcomes change across observation rounds in response to interventions.<n>We highlight causal message passing as an instantiation of this method in dense networks.<n>We discuss the limits of this approach, showing that strong temporal trends or endogenous interference can undermine identification.
  arXiv  Detail & Related papers  (2025-11-26T18:53:46Z)
• Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network [0.39462888523270856]
  Network interference occurs when treatments assigned to some units affect the outcomes of others.<n>Traditional approaches often assume that the observed network correctly specifies the interference structure.<n>We introduce a framework for estimating causal effects when only proxy networks are available.
  arXiv  Detail & Related papers  (2025-05-13T09:46:30Z)
• Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations [76.2226569692207]
  Correlations between input parameters play a crucial role in many scientific classification tasks.<n>We present a new adversarial attack algorithm called Random Distribution Shuffle Attack (RDSA)<n>We demonstrate the RDSA effectiveness on six classification tasks.
  arXiv  Detail & Related papers  (2025-01-09T21:45:09Z)
• Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding [2.654975444537834]
  Key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding.<n>We propose network causal effect estimation strategies that provide unbiased and consistent estimates.<n>We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.
  arXiv  Detail & Related papers  (2024-11-02T22:12:44Z)
• Estimating Peer Direct and Indirect Effects in Observational Network Data [16.006409149421515]
  We propose a general setting which considers both peer direct effects and peer indirect effects, and the effect of an individual's own treatment. We use attention mechanisms to distinguish the influences of different neighbors and explore high-order neighbor effects through graph neural networks. Our theoretical findings have the potential to improve intervention strategies in networked systems, with applications in areas such as social networks and epidemiology.
  arXiv  Detail & Related papers  (2024-08-21T10:02:05Z)
• Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects [17.44202934049009]
  We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework. We demonstrate our method is accurate, robust, and scalable via an extensive simulation study.
  arXiv  Detail & Related papers  (2024-03-17T20:23:42Z)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization [52.72490784720227]
  REASON consists of Topological Causal Discovery and Individual Causal Discovery. The Topological Causal Discovery component aims to model the fault propagation in order to trace back to the root causes. The Individual Causal Discovery component focuses on capturing abrupt change patterns of a single system entity.
  arXiv  Detail & Related papers  (2023-02-03T20:17:45Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [109.17155002599978]
  We consider the violation of the classical no-interference assumption with units connected by a network.<n>For tractability, we consider a known network that describes how interference may spread.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Vulnerability Under Adversarial Machine Learning: Bias or Variance? [77.30759061082085]
  We investigate the effect of adversarial machine learning on the bias and variance of a trained deep neural network. Our analysis sheds light on why the deep neural networks have poor performance under adversarial perturbation. We introduce a new adversarial machine learning algorithm with lower computational complexity than well-known adversarial machine learning strategies.
  arXiv  Detail & Related papers  (2020-08-01T00:58:54Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.