Forecasting Causal Effects of Interventions versus Predicting Future Outcomes.

Area of Science:

• Causal Inference
• Statistical Modeling
• Biostatistics

Background:

• Structural equation modeling (SEM) researchers often require advanced causal inference methods.
• Pearl's directed acyclic graph (DAG) approach offers a robust framework for causal inference.
• Distinguishing between intervention forecasts and future predictions is crucial in statistical analysis.

Purpose of the Study:

• To provide a didactic presentation and extension of Pearl's DAG-based causal inference approach for SEM researchers.
• To differentiate between intervention forecasts and future predictions of outcome variables.
• To extend the DAG approach to incorporate additive random effects for personalized intervention analysis.

Main Methods:

• Utilized a cross-lagged panel design to illustrate key concepts of causal inference.
• Extended Pearl's DAG-based approach to include additive random effects.
• Derived optimal person-specific treatment levels based on simulated data.

Main Results:

• Successfully distinguished between forecasts of intervention outcomes and predictions of future values.
• Demonstrated the ability to analyze mean, variance, and range probabilities of outcome variables.
• Showcased that optimal treatment levels can vary significantly across individuals.

Conclusions:

• The extended DAG approach effectively distinguishes between intervention forecasts and predictions.
• Incorporating additive random effects allows for the estimation of both average and person-specific intervention effects.
• Optimal treatment levels are individualized, necessitating tailored intervention strategies.