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Mediation: R package for causal mediation analysis

Author(s): Tingley, Dustin; Yamamoto, Teppei; Hirose, Kentaro; Keele, Luke; Imai, Kosuke

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Abstract: © 2014, Journal of Statistical Software. All rights reserved. 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.
Publication Date: Aug-2014
Citation: Tingley, D, Yamamoto, T, Hirose, K, Keele, L, Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59 (5), 1 - 38. doi:10.18637/jss.v059.i05
DOI: doi:10.18637/jss.v059.i05
ISSN: 1548-7660
Pages: 1 - 38
Type of Material: Journal Article
Journal/Proceeding Title: Journal of Statistical Software
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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