Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects

Trang Quynh Nguyen, Elizabeth L. Ogburn, Ian Schmid, Elizabeth B. Sarker, Noah Greifer, Ina M. Koning, Elizabeth A. Stuart

Research output: Contribution to journalArticlepeer-review


This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as in-puts two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted models), and show how a range of estimators can be generated, with different modeling requirements and robustness properties. The primary goal is to help build intuitive appre-ciation for robust estimation that is conducive to sound practice. We do this by visualizing the target estimand and the estimation strategies. A second goal is to provide a “menu” of estimators that practitioners can choose from for the estimation of marginal natural (in)direct effects. The estimators generated from this exercise include some that coincide or are similar to existing estimators and others that have not previously appeared in the literature. We note several different ways to estimate the weights for cross-world weighting based on three expressions of the weighting function, including one that is novel; and show how to check the resulting covari-ate and mediator balance. We use a random continuous weights bootstrap to obtain confidence intervals, and also derive general asymptotic variance formulas for the estimators. The estimators are illustrated using data from an adolescent alcohol use prevention study. R-code is provided.

Original languageEnglish (US)
Pages (from-to)1-41
Number of pages41
JournalStatistics Surveys
StatePublished - 2023


  • Causal mediation analysis
  • method visualization
  • natural (in)direct effects
  • robust estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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