Equilibrium Causal Models: Connecting Dynamical Systems Modeling and Cross-Sectional Data Analysis

Area of Science:

• Psychological science
• Causal inference
• Dynamical systems

Background:

• Psychological phenomena often involve complex systems evolving over time within individuals.
• Current research frequently relies on cross-sectional data, limiting causal insights into these dynamic processes.

Purpose of the Study:

• Introduce Equilibrium Causal Models (ECMs) to psychology.
• Determine conditions for inferring causal relationships from cross-sectional data.
• Enable the study of within-person processes using static measurements.

Main Methods:

• Develop and apply Equilibrium Causal Models (ECMs).
• Utilize cross-sectional data capturing the system's resting state.
• Integrate methods from psychological measurement and causal discovery.

Main Results:

• ECMs allow causal inferences about long-term intervention effects from cross-sectional data.
• ECMs accommodate cyclic causal relationships within psychological systems.
• Demonstrate the possibility of learning about within-person dynamics from static data.

Conclusions:

• Equilibrium Causal Models (ECMs) offer a novel approach to studying dynamic psychological systems.
• Cross-sectional data, under specific conditions, can yield valuable causal insights.
• Future research should leverage ECMs and integrated analytical tools for richer understanding.