mediation: R Package for Causal Mediation Analysis
TL;DR: The mediation package implements a comprehensive suite of statistical tools for conducting causal mediation analysis in applied empirical research and implements a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Improved standardization of transcribed digital specimen data
TL;DR: This paper makes recommendations to standards organizations, software developers, data scientists and transcribers to improve transcription data with the specific aim of improving interoperability between collection datasets.
Developmental shifts in computations used to detect environmental controllability
TL;DR: This paper found that while children were able to distinguish controllable and uncontrollable conditions, accuracy of controllability assessments improved with age, whereas older participants more effectively recruited their task structure knowledge to make highly informative interventions.
15
An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm
Osval A. Montesinos-López,Francisco Javier Luna-Vazquez,Abelardo Montesinos-López,Philomin Juliana,Ravi P. Singh,José Crossa +5 more
TL;DR: This software will assist plant breeders to make predictions of unobserved primary traits from other observed secondary traits and could be useful in conventional phenotypic selections or in genomic selection.
15
Species densities, biological interactions and benthic ecosystem functioning: an in situ experiment
TL;DR: The results reaffirm the direct functional importance of certain species in a natural ecosystem and highlight the indirect importance of other species to which their density is tightly coupled.
Bayesian Causal Mediation Analysis with Latent Mediators and Survival Outcome
TL;DR: A joint modeling approach that incorporates latent traits into causal mediation analysis with multiple mediators and a survival outcome is developed and applied to investigate the causal effects of APOE- allele on the disease progression.
15
References
•Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
410.8K
The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.
Reuben M. Baron,David A. Kenny +1 more
TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
TL;DR: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
31.1K
•Book
Experimental and Quasi-Experimental Designs for Generalized Causal Inference
William R. Shadish,Thomas D. Cook,Donald T. Campbell +2 more
- 01 Jan 2001
TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
15.3K
Linear Mixed-Effects Models using 'Eigen' and S4
Douglas M. Bates,Martin Maechler,Ben Bolker,Steven C. Walker +3 more
- 06 Oct 2015
TL;DR: The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen``glue''.
9K