In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. View the entire collection of UVA Library StatLab articles. Mediation analysis was performed based on the counter-factual framework and the interventional effect (Vansteelandt and Daniel, 2017; Chan and Leung, 2020). Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. ``Causal Mediation Analysis Using R,'' in Advances in Social Science Research Using R, ed. mediation — Causal Mediation Analysis. Causal Mediation Analysis Using R K. Imai, L. Keele, D. Tingley, and T. Yamamoto Abstract Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Back to Research Page. In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010), Imai, Keele and Tingley (2010), Imai, Tingley and Yamamoto (2013), Imai and Yamamoto (2013) and Yamamoto (2013). mediation: R Package for Causal Mediation Analysis. 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. Abstract. (2010). In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In addition, new research designs have been proposed for identifying causal mechanisms. 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. In many scienti c disciplines, the goal of researchers is not only estimating causal e ects of a treatment but also understanding the process in which the treatment causally a ects the outcome. Journal of Statistical Software , 59 (5). Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal e ects. In this paper, we introduce a full featured R package, mediation, for studying causal mecha-nisms. The package is organized into two distinct approaches. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. The second table shows the results from mediation analysis. For questions or clarifications regarding this article, contact the UVA Library StatLab: statlab@virginia.edu. The mediation package allows users to (1) investigate the role of causal mechanisms Causal mediation analysis is fre- Results from mediation analysis. We implement parametric and non parametric mediation analysis. 2 Causal Mediation Analysis towards a more exible estimation strategy. Download Paper. Bommae Kim Statistical Consulting Associate University of Virginia Library In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. Mediation: R package for causal mediation analysis. Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. H. D. Vinod, New York: Springer (Lecture Notes in Statistics), pp.129-154. mediation: Causal Mediation Analysis version 4.5.0 from CRAN The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. R package mediation: causal mediation analysis . About This is a read-only mirror of the CRAN R package repository.