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  3. A Tutorial On Causal Network Simulation And Exploration Using The Causalnet R Package.
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  3. A Tutorial On Causal Network Simulation And Exploration Using The Causalnet R Package.

Related Experiment Video

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A tutorial on causal network simulation and exploration using the causalnet R package.

Kyuri Park1, Vítor V Vasconcelos2,3, Mike Lees2,3

  • 1Computational Science Lab, Informatics Institute, University of Amsterdam, PO Box 94323, Amsterdam, 1090GH, The Netherlands. kyurheep@gmail.com.

Behavior Research Methods
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The causalnet R package helps researchers explore how network structures impact psychological dynamics. It aids in evaluating competing theories by simulating system behavior under different causal network configurations.

Keywords:
Causal networkNetwork enumerationPsychological modelingSymptom dynamics

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Area of Science:

  • Psychological modeling
  • Network science
  • Computational social science

Background:

  • Understanding network structure's influence on system dynamics is crucial for psychological modeling.
  • Current methods may not fully capture the complexity of causal relationships in psychological systems.

Purpose of the Study:

  • Introduce the causalnet R package for systematically enumerating and simulating directed network structures.
  • Enable researchers to evaluate competing psychological theories by linking structural assumptions to dynamic outcomes.

Main Methods:

  • Systematic enumeration of candidate directed networks from adjacency templates.
  • Imposing directional constraints based on prior theory or time-series models.
  • Dynamic simulations using nonlinear or linear models to compare outcomes across configurations.

Main Results:

  • The causalnet package facilitates theory- and evidence-constrained exploration of directed network structures.
  • Simulation-based screening of dynamic implications against empirical targets is supported.
  • The workflow aids in adjudicating competing psychological theories by examining predicted dynamic signatures.

Conclusions:

  • causalnet provides a framework for systematically exploring how causal architecture and interaction dynamics shape psychological processes.
  • This approach enhances the evaluation of theoretical accounts when causal direction is uncertain.
  • Facilitates a deeper understanding of emergent dynamics in psychological systems.