Statistical Approaches for Enhancing Causal Interpretation of the M to Y Relation in Mediation Analysis

David Mackinnon, Angela G. Pirlott

Research output: Contribution to journalArticlepeer-review

170 Scopus citations


Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.

Original languageEnglish (US)
Pages (from-to)30-43
Number of pages14
JournalPersonality and Social Psychology Review
Issue number1
StatePublished - Feb 14 2015


  • causal inference
  • confounder bias
  • mediation

ASJC Scopus subject areas

  • Social Psychology


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