Identifying causal mechanisms in health care interventions using classification tree analysis.

RATIONALE, AIMS, AND OBJECTIVES: Mediation analysis identifies causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that mediates the relationship between the treatment and outcome. This paper introduces classification tree analysis (CTA), a machine-learning procedure, as an alternative to conventional methods for analysing mediation effects.

METHOD: Using data from the JOBS II study, we compare CTA to structural equation models (SEMs) by assessing their consistency in revealing mediation effects on 2 outcomes; reemployment (a binary variable) and depressive symptoms (a continuous variable). Because study participants were not randomized sequentially to both treatment and mediator, an additional model was generated including baseline covariates to strengthen the validity of some key identifying assumptions required of all mediation analyses.

RESULTS: Using SEM, no statistically significant treatment or mediated effects were found for either outcome. In contrast, CTA found a significant treatment effect for reemployment (P = .047) and a mediated pathway for individuals in the treatment group (P = .014). No CTA model could be generated for the reemployment outcome. When covariates were added to the model, CTA found numerous interactions, while SEM found no effects.

CONCLUSIONS: CTA may uncover mediation effects where conventional approaches do not, because CTA does not require any assumptions about the distribution of variables nor of the functional form of the model, and CTA will systematically identify all statistically viable interactions. The versatility of CTA enables the investigator to explore the theorized underlying causal mechanism of an intervention in a much more comprehensive manner than conventional mediation analytic approaches.