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.
Original language | English |
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Pages (from-to) | 1-38 |
Number of pages | 38 |
Journal | Journal of Statistical Software |
Volume | 59 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2014 Aug 1 |
Externally published | Yes |
Keywords
- Causal mechanisms
- Mediation analysis
- Mediation, R.
ASJC Scopus subject areas
- Software
- Statistics and Probability
- Statistics, Probability and Uncertainty