mediation: R Package for Causal Mediation Analysis

By Dustin Tingley, Teppei Yamamoto, Kentaro Hirose, Luke Keele, Kosuke Imai. 2014

4,010 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4b4227f74203059ba215b07dd3b4d6047fcdcae2

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Citations in the corpus

What Predicts Positive Life Events that Influence the Course of Depression? A Longitudinal Examination of Gratitude and Meaning in Life (2017, positive): “The R packages ‘‘lme4’’, ‘‘lmerTest’’ and ‘‘mediation’’ were all used to conduct the following statistical analyses (Bates et al. 2015; Kuznetsova et al. 2015; Tingley et al. 2014).”
Mediation Analysis: A Practitioner's Guide. (2016, neutral): “DISCLOSURE STATEMENT”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “With increased attention to causal inference, the methods for causal mediation analysis have also developed rapidly (see, e.g., Hicks & Tingley, 2011; Keele et al., 2015; Tingley et al., 2014).”
Feeling Authentic on Social Media: Subjective Authenticity Across Instagram Stories and Posts (2021, methodology): “Mediation analysis was conducted with the mediation package (version 4.5.0; Tingley et al., 2014), and p values were estimated with quasi-Bayesian confidence intervals computed from 2,000 Monte Carlo simulations.”
Advances in mediation analysis: a survey and synthesis of new developments. (2015, background): “Recent illustrations of MSEM for assessing mediation include Card (2012), Li & Beretvas (2013), and Tofighi & Thoemmes (2014).”
Operational Transparency on Crowdfunding Platforms: Effect on Donations for Emergency Response (2019, methodology): “Using the Mediation package in R (Tingley et al. 2014), we run 1000 simulations with a sample of 305 observations to estimate the nonparametric bootstrap (3) Non-Operational Update vs. Work-Related Operational Update.”
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments (2013, methodology): “All of our proposed methods can be implemented through the open-source software program mediation (Imai et al. 2010b; Tingley et al. 2012), which is freely available at the Comprehensive R Archive Network (CRAN, http://cran.r-project. org/package¼mediation).”