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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.|
|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|
|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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