Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. (2010). ``Causal Mediation Analysis Using R,'' in Advances in Social Science Research Using R, ed. H. D. Vinod, New York: Springer (Lecture Notes in Statistics), pp.129-154.



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.

© Kosuke Imai
  Last modified: Fri Jan 22 13:14:01 EST 2010