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This study introduces Bayesian causal mediation forests for analyzing latent variables in causal mediation analysis (CMA). The method reveals reasoning training improves daily activities via nonlinear reasoning ability in older adults.

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Area of Science:

  • Statistics
  • Psychology
  • Biostatistics

Background:

  • Causal mediation analysis (CMA) with latent variables is challenging for traditional methods.
  • Existing approaches struggle with nonlinear relationships and measurement error.

Purpose of the Study:

  • To develop a novel Bayesian approach for CMA with latent variables.
  • To address limitations of traditional structural equation modeling in nonlinear contexts.

Main Methods:

  • Utilized Bayesian causal mediation forests for structural relationships.
  • Employed a full hierarchical Bayesian model and a composite score approximation for latent variable inference.
  • Validated through simulation experiments and application to the ACTIVE study data.

Main Results:

  • Identified a nonlinear relationship between daily activity performance (outcome) and reasoning ability (mediator).
  • Demonstrated that reasoning training positively impacts daily activities through reasoning ability.

Conclusions:

  • Bayesian causal mediation forests offer a flexible approach for complex mediation models.
  • The findings highlight the effectiveness of cognitive training interventions in older adults.