Direct and indirect effects for neighborhood-based clustered and longitudinal data

Area of Science:

• Causal inference
• Epidemiology
• Social and behavioral sciences

Background:

• Interventions are often administered at the group level (e.g., neighborhoods).
• Individual outcomes can be influenced directly and indirectly through group-level changes.
• Existing causal inference methods may not fully capture these complex group-level dynamics.

Purpose of the Study:

• To define direct and indirect effects in clustered settings with group-level treatments.
• To establish identification conditions for controlled direct effects and natural direct/indirect effects.
• To explore the application of these definitions in neighborhood research and multilevel modeling.

Main Methods:

• Development of causal definitions for direct and indirect effects in clustered data.
• Formulation of identification conditions for various effect types.
• Consideration of both single-point-in-time and time-varying interventions.
• Discussion of stability and no-interference assumptions and potential relaxations.

Main Results:

• Formal definitions for direct and indirect effects are provided for group-level interventions.
• Identification conditions are derived for controlled direct effects and natural direct/indirect effects.
• The framework accommodates complex interactions between individual and group-level influences.

Conclusions:

• The proposed definitions and identification conditions enhance causal inference in clustered settings.
• This work is particularly relevant for neighborhood research and multilevel modeling.
• The study offers a foundation for analyzing complex interventions with both individual and group-level pathways.