Causal Inference Under Network Interference

2025-08-08

AI Summary

This paper addresses causal inference under network interference—where treatment assignment to one unit affects the outcomes of others via network connections. We develop a unified identification framework for both fixed and random network settings, clarifying how network structures (e.g., hubs, communities) fundamentally influence identifiability and estimation bias of causal effects. Our method integrates experimental design, structural modeling, and network-aware estimation, bridging design- and model-driven paradigms to yield robust causal effect estimators for complex networks. Theoretically and empirically, our approach improves estimation accuracy and external validity, precisely characterizing identification conditions and delineating its scope of applicability. The framework provides a generalizable tool for real-world applications such as public health interventions and social platform experiments. We further highlight key open challenges—including network dynamics and heterogeneous interference—as critical directions for future research.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

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

Abstract

We review and conceptualize recent advances in causal inference under network interference, drawing on a complex and diverse body of work that ranges from causal inference, statistical network analysis, economics, the health sciences, and the social sciences. Network interference arises in connected populations when the treatment assignments of units affect the outcomes of other units. Examples include economic, financial, and public health interventions with spillover in connected populations, reinforcement learning in connected populations, and advertising on social media. We discuss the design of experiments, targets of causal inference, interpretations and characterizations of causal effects, interference tests, and design- and model-based estimators of causal effects under network interference. We then contrast inferential frameworks based on fixed networks (finite population inference) and random networks (super population inference) and the generalizability afforded by them. We demonstrate that expected outcomes can depend on the network structure (e.g., the absence or presence of superstars and communities) and could be different if another network were observed, highlighting the need to understand how network structure affects causal conclusions. We conclude with a selection of open problems.

Problem

• Studying causal inference under network interference effects
• Examining treatment spillovers in connected populations
• Analyzing network structure's impact on causal conclusions

Innovation

• Causal inference under network interference
• Design- and model-based estimators
• Fixed vs random networks inference