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. 2020;27(6):975-984.
doi: 10.1080/10705511.2020.1777133. Epub 2020 Aug 3.

Causal Mediation Programs in R, M plus, SAS, SPSS, and Stata

Matthew J Valente  1 , Judith J M Rijnhart  2 , Heather L Smyth  3 , Felix B Muniz  3 , David P MacKinnon  3
Affiliations

Causal Mediation Programs in R, M plus, SAS, SPSS, and Stata

Matthew J Valente et al. Struct Equ Modeling. 2020.

Abstract

Mediation analysis is a methodology used to understand how and why an independent variable (X) transmits its effect to an outcome (Y) through a mediator (M). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of mediation analysis. There are several programs available to estimate causal mediation effects, but these programs differ substantially in data set up, estimation, output, and software platform. To compare these programs, an empirical example is presented, and a single mediator model with XM interaction was estimated with a continuous mediator and a continuous outcome in each program. Even though the software packages employ different estimation methods, they do provide similar causal effect estimates for mediation models with a continuous mediator and outcome. A detailed explanation of program similarities, unique features, and recommendations are discussed.

Keywords: causal effects; counterfactual; estimation; mediation; software.

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Figures

Figure 1.
Figure 1.
(A) Path diagram depicting the single mediator model with treatment by mediator interaction (XM). (B) Path diagram depicting the single mediator model with treatment by mediator interaction (XM) and baseline covariate (C) confounding the X-M, M-Y, and X-Y relations.
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