A tutorial on causal network simulation and exploration using the causalnet R package.

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.