Causal mediation analysis is widely used across many disciplines to
investigate possible causal mechanisms. Such an analysis allows
researchers to explore causal pathways, going beyond the
estimation
of simple causal effects. Recently,
Imai, Keele and Yamamoto (2008) and
Imai, Keele, and Tingley
(2009) developed general algorithms to
estimate
causal mediation effects with the variety of data types that are
often encountered in practice. The new algorithms can estimate
causal mediation effects for linear and nonlinear relationships,
with parametric and nonparametric models, with continuous and
discrete mediators, and various types of outcome variables. In
this
paper, we show how to implement these algorithms in the
statistical
computing language
R. Our easy-to-use software, mediation,
takes advantage of the object-oriented programming nature of the
R language and allows researchers to estimate causal
mediation
effects in a straightforward manner. Finally, mediation also
implements sensitivity analyses which can be used to formally
assess
the robustness of findings to the potential violations of the
key
identifying assumption. After describing the basic structure of
the
software, we illustrate its use with several empirical examples.