mediation: R package for causal mediation analysis
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Tingley-2014-mediation.pdf
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644.67 KB
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Author(s)
Tingley, Dustin
Yamamoto, Teppei
Hirose, Kentaro
Keele, Luke
Imai, Kosuke
Date Issued
August 2014
Journal
Journal of Statistical Software
Publisher
UCLA Statistics/American Statistical Association
Citation
Tingley, Dustin,Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. "mediation: R package for causal mediation analysis." Journal of Statistical Software Vol. 59, Issue 5 (September 2014).
Version
Final published version
Abstract
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Creative Commons Attribution
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DOI of Published Version
http://www.jstatsoft.org/v59/i05
