Quantum mechanics

Algorithm

World Wide Web

Engineering

Statistics

Causal inference

Centrality

Node (physics)

Theoretical computer science

Computer science

Complex network

Network science

Conditional independence

Physics

Graph

Mathematics

Causal model

Inference

Econometrics

Artificial intelligence

Combinatorics

Structural engineering

Network theory

Causality (physics)

Directed acyclic graph

Scientific Reports

Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions.

Node centrality measures are a poor substitute for causal inference