title: Bayesian Causal Mediation Analysis for Group Randomized Designs with Homogeneous and Heterogeneous Effects: Simulation and Case Study.
abstract: A fully Bayesian approach to causal mediation analysis for group-randomized designs is presented. A unique contribution of this article is the combination of Bayesian inferential methods with G-computation to address the problem of heterogeneous treatment or mediator effects. A detailed simulation study shows that this approach has excellent frequentist properties, particularly in the case of small sample sizes with accurate informative priors. The simulation study also demonstrates that the proposed approach can take into account heterogeneous treatment or mediator effects without bias. A case study using data from a school-based randomized intervention designed to increase parent social capital leading to improved behavioral and academic outcomes in children is offered to illustrate the Bayesian approach to causal mediation in group-randomized designs.
authors.affiliation: a Department of Educational Psychology , University of Wisconsin-Madison.
authors.forename: Soojin
authors.initials: S
authors.lastname: Park
authors.affiliation: a Department of Educational Psychology , University of Wisconsin-Madison.
authors.forename: David
authors.initials: D
authors.lastname: Kaplan
mesh_terms: D001499:Bayes Theorem; D035841:Behavioral Research; D002648:Child; D002652:Child Behavior; D003198:Computer Simulation; D006801:Humans; D011897:Random Allocation; D012107:Research Design; D012574:Schools