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A regression-with-residuals method for analyzing causal mediation: The rwrmed package

Author

Listed:
  • Ariel Linden

    (Linden Consulting Group)

  • Chuck Huber

    (StataCorp)

  • Geoffrey T. Wodtke

    (University of Chicago)

Abstract

In this article, we introduce the rwrmed package, which performs mediation analysis using the methods proposed by Wodtke and Zhou (2020, Epi- demiology 31: 369–375). Specifically, rwrmed estimates interventional direct and indirect effects in the presence of treatment-induced confounding by fitting models for 1) the conditional mean of the mediator given the treatment and a set of base- line confounders and 2) the conditional mean of the outcome given the treatment, mediator, baseline confounders, and a set of treatment-induced confounders that have been residualized with respect to the observed past. Interventional direct and indirect effects are simple functions of the parameters in these models when they are correctly specified and when there are no unobserved variables that confound the treatment-outcome, treatment-mediator, or mediator-outcome relationships. When no treatment-induced confounders are specified, rwrmed produces natural direct and indirect effect estimates.

Suggested Citation

  • Ariel Linden & Chuck Huber & Geoffrey T. Wodtke, 2021. "A regression-with-residuals method for analyzing causal mediation: The rwrmed package," Stata Journal, StataCorp LP, vol. 21(3), pages 559-574, September.
    • Handle: RePEc:tsj:stataj:v:21:y:2021:i:3:p:559-574
    DOI: 10.1177/1536867X211045511
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