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Conference on Quantitative
Social Science Research Using R
(click on the title to download the paper)
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Kosuke Imai (presenter), Luke Keele, Dustin Tingley, and Teppei Yamamoto
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Causal Mediation Analysis in R
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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. (Last Revised June, 2009)
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