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Related Experiment Video

Updated: Aug 10, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Finding influential subjects in a network using a causal framework.

Youjin Lee¹, Ashley L Buchanan², Elizabeth L Ogburn³

¹Department of Biostatistics, Brown University, Providence, Rhode Island, USA.

|February 14, 2023

Summary

This summary is machine-generated.

Identifying influential individuals in networks is key for public health interventions. This study defines a causal influence measure to better target interventions and improve health outcomes.

Keywords:

causal inference centrality contagion interference

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Published on: December 7, 2021

Area of Science:

Network science
Causal inference
Public health

Background:

Identifying influential individuals in networks is crucial for maximizing intervention impact in public health.
Existing influence measures often rely on network structure or diffusion models, with an implicit causal assumption.
The operative notion of influence in network research is often causal: identifying nodes for intervention to achieve maximal network-wide effects.

Approach:

Define a causal notion of influence using potential outcomes framework.
Review existing network influence measures, such as node centrality.
Conduct simulation studies to compare causal influence with traditional centrality measures.

Key Points:

Centrality measures may not always align with causal influence.
The study provides a framework for understanding and measuring causal influence in networks.
Simulation results show conditions under which centrality measures approximate causal influence.

Conclusions:

A causal definition of influence is essential for effective network interventions.
Understanding the assumptions behind centrality measures is critical for their application.
The proposed causal framework offers a more robust approach to identifying influential individuals in network studies.