mediation: R package for causal mediation analysis
Author(s)Tingley, Dustin; Yamamoto, Teppei; Hirose, Kentaro; Keele, Luke; Imai, Kosuke
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Department of Political Science
Journal of Statistical Software
UCLA Statistics/American Statistical Association
Tingley, Dustin,Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. "mediation: R package for causal mediation analysis." Journal of Statistical Software Vol. 59, Issue 5 (September 2014).
Final published version