title: Classical and causal inference approaches to statistical mediation analysis.
abstract: BACKGROUND
  Although there is a broad consensus on the use of statistical procedures for mediation analysis in psychological research, the interpretation of the effect of mediation is highly controversial because of the potential violation of the assumptions required in application, most of which are ignored in practice.
  
  
  METHODS
  This paper summarises two currently independent procedures for mediation analysis, the classical/SEM and causal inference/CI approaches, together with the statistical assumptions required to estimate unbiased mediation effects, in particular the existence of omitted variables or confounders. A simulation study was run to test whether violating the assumptions changes the estimation of mediating effects.
  
  
  RESULTS
  The simulation study showed a significant overestimation of mediation effects with latent confounders.
  
  
  CONCLUSIONS
  We recommend expanding the classical with the causal inference approach, which generalises the results of the first approach to mediation using a common estimation method and incorporates new tools to evaluate the statistical assumptions. To achieve this goal, we compare the distinguishing features of recently developed software programs in R, SAS, SPSS, STATA and Mplus.
authors.affiliation: Universidad de Murcia.
authors.forename: Manuel
authors.initials: M
authors.lastname: Ato García
authors.forename: Guillermo
authors.initials: G
authors.lastname: Vallejo Seco
authors.forename: Ester
authors.initials: E
authors.lastname: Ato Lozano
mesh_terms: D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D015233:Models, Statistical; D012044:Regression Analysis; D012984:Software