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Causal Mediation Analysis in R

Causal Mediation Analysis in R,Dustin Tingley,Teppei Yamamoto

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Causal Mediation Analysis in R   (Citations: 5)
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Abstract Causal mediation analysis is widely used across many,disciplines to investigate possible causal mechanisms. Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal eects. Recently, Imai, Keele, and Yamamoto (2008) and Imai, Keele, and Tingley (2009) developed general algorithms to estimate causal mediation eects,with the variety of data types that are often encountered in practice. The new algorithms can estimate causal mediation eects 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 eects in a straightforward manner. Finally, mediation also implements sensitivity analyses which can be used to formally assess the robustness of,ndings 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. The most recent version (along with all previous versions) of the R package, mediation, is available for download
Published in 2009.
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