Assessing natural direct and indirect effects through multiple pathways

Am J Epidemiol. 2014 Feb 15;179(4):513-8. doi: 10.1093/aje/kwt270. Epub 2013 Nov 20.


Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190-195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas).

Keywords: causal inference; mediation; multiple mediators.

MeSH terms

  • Causality*
  • Humans
  • Models, Statistical*
  • Proportional Hazards Models