Tingley, DustinYamamoto, TeppeiHirose, KentaroKeele, LukeImai, Kosuke2019-01-192014Tingley, Dustin, Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. 2014. “Mediation: R Package for Causal Mediation Analysis.” Journal of Statistical Software 59 (5). https://doi.org/10.18637/jss.v059.i05.1548-7660http://nrs.harvard.edu/urn-3:HUL.InstRepos:38057805In 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.en-USJournal Article2019-01-1910.18637/jss.v059.i05