Connections as treatment: causal inference with edge interventions in networks

2026-01-12

AI Summary

This study addresses a critical gap in causal inference by shifting focus from node-level interventions to interventions on network edges—such as transportation links or social ties—whose causal effects on outcomes are otherwise difficult to assess. The authors propose the first causal inference framework specifically designed for edge interventions, treating the presence of an edge as a treatment variable. Under the assumption of no unmeasured confounding in edge assignment, they develop an inverse probability weighting estimator, leveraging Exponential Random Graph Models (ERGMs) to model and estimate edge formation probabilities. Theoretical analysis establishes the consistency and asymptotic normality of the proposed estimator. Empirically, the method is applied to China’s railway network, successfully quantifying the causal impact of rail connections on regional economic development, thereby offering a novel paradigm for causal analysis of structural interventions in networks.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Causal inference has traditionally focused on interventions at the unit level. In many applications, however, the central question concerns the causal effects of connections between units, such as transportation links, social relationships, or collaborative ties. We develop a causal framework for edge interventions in networks, where treatments correspond to the presence or absence of edges. Our framework defines causal estimands under stochastic interventions on the network structure and introduces an inverse probability weighting estimator under an unconfoundedness assumption on edge assignment. We estimate edge probabilities using exponential random graph models, a widely used class of network models. We establish consistency and asymptotic normality of the proposed estimator. Finally, we apply our methodology to China's transportation network to estimate the causal impact of railroad connections on regional economic development.

Problem

• causal inference
• edge interventions
• networks
• treatment effects
• causal effects

Innovation

• edge interventions
• causal inference
• inverse probability weighting
• exponential random graph models
• network causality