Causal Mediation Analysis Using R

  • K. Imai
  • L. Keele
  • D. Tingley
  • T. Yamamoto
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 196)


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 effects. Recently, Imai et al. (2008) [3] and Imai et al. (2009) [2] 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.


Quantile Regression Nonparametric Bootstrap Mediator Variable Semiparametric Regression Quantile Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Department of PoliticsPrinceton UniversityPrincetonUSA
  2. 2.Department of Political ScienceOhio State UniversityColumbusUSA

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