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Node centrality measures are a poor substitute for causal inference.

Fabian Dablander1, Max Hinne2

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Network models are useful, but high centrality doesn't always mean causal importance. Researchers should be cautious when interpreting network nodes to avoid suboptimal interventions in systems modeled as directed acyclic graphs (DAGs).

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Area of Science:

  • Network science
  • Causal inference
  • Graph theory

Background:

  • Network models visualize complex systems using nodes and edges.
  • Node centrality measures are commonly used to assess node importance.
  • Interpreting high centrality as causal influence is intuitive but potentially flawed.

Purpose of the Study:

  • To investigate the relationship between causal influence and node centrality measures in network models.
  • To determine if high centrality reliably indicates causal importance within a directed acyclic graph (DAG) framework.

Main Methods:

  • Utilized the causal framework based on directed acyclic graphs (DAGs).
  • Analyzed the correlation between causal influence and various node centrality measures.

Main Results:

  • The correlation between causal influence and most node centrality measures is weak.
  • Eigenvector centrality showed a stronger correlation with causal influence compared to other measures.
  • This suggests that high centrality does not always equate to significant causal influence.

Conclusions:

  • Interpreting nodes with high centrality as causally important can lead to incorrect conclusions.
  • Misinterpreting centrality may result in suboptimal interventions in systems modeled as DAGs.
  • Emphasizes the need for careful consideration of network structure and causal assumptions.