Journal of Statistical Software (Sep 2014)

mediation: R Package for Causal Mediation Analysis

  • Dustin Tingley,
  • Teppei Yamamoto,
  • Kentaro Hirose,
  • Luke Keele,
  • Kosuke Imai

DOI
https://doi.org/10.18637/jss.v059.i05
Journal volume & issue
Vol. 59, no. 1
pp. 1 – 38

Abstract

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In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.