Estimating the mechanisms that connect explanatory variables with the explained variable, also known as mediation analysis, is central to a variety of socialscience fields, especially psychology. Mediation analysis allows one to investigate biological pathways and assess their relative contribution to observed health effects imai and others, 2011. Our software, mediation, also allows users to conduct sensitivity analyses with only a single additional line of syntax, as illustrated in imai et al. The package is organized into two distinct approaches.
The article continues with instructions for using the software developed sas and spss and a description of the output is provided. Description usage arguments details warning authors references see also. This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may. Causal mediation analysis raymond hicks, dustin tingley. Mediation analysis has been around a long time, though its popularity has varied between disciplines and over the years. Causal mediation analysis raymond hicks, dustin tingley, 2011. Their approach to mediation analysis relies on monte carlo methods. Identification, inference and sensitivity analysis for causal. Our approach is applicable to a wide range of statistical models, going beyond the traditional linear structural equation framework. Estimating the mechanisms that connect explanatory variables with the explained variable, also known as mediation analysis, is central to a variety of socialscience fields, especially.
The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of. Practical guidance for conducting mediation analysis with. The package won the 2015 polmeth statistical software award. I think there are two approaches to this the classic baron and kenny 1986 and the new one by preacher, rucker and hayes 2007 id like to know how to do both approaches in r. The goal of such an analysis is to investigate alternativecausal mechanisms by examining the roles of intermediate variablesthat lie in the causal path between the treatment and outcomevariables. The latter group has written mediation, a software package for the r. We conclude by providing an example of mediation analysis performed using the mediation macros. Mediation is the process through which an exposure causes disease. The mediation package consists of several main functions as well as various methods for summarizing output from these functions e. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain.
M denotes the mediator, which temporally succeeds the. In addition to the estimation of causal mediation effects, the software allows researchers to conduct sensitivity analysis for certain parametric. This is the primary goal of causal mediation analysis. Identification, inference, and sensitivity analysis for. Identification, inference and sensitivity analysis for causal mit. Attention is given to the confounding assumptions required for a causal interpretation of. Mediation analysis partitions an exposureoutcome effect into an indirect effect via a change in a mediator and a direct effect via other mechanisms baron and kenny, 1986. We argue and demonstrate that this is problematic for 3 reasons. Paper software identification and sensitivity analysis for multiple causal mechanisms. Id like to know if anybody can provide a stepbystep how to on how to use mediation analysis using keele, tingley, yamamoto and imai s mediation package.
Using the modelbased approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. Core structure of the mediation package as of version 4. Sep 05, 2019 this package performs the methods and suggestions in imai, keele and yamamoto 2010, imai, keele and tingley 2010, imai, tingley and yamamoto 20, imai and yamamoto 20 and yamamoto 20.
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. Morgan and others, 20, causal inference rubin, 1991, 2004. While some fields have been attracted to the potential of mediation models to identify pathways, or mechanisms, through which an independent variable affects an outcome, others have been skeptical that the analysis of mediated relationships can ever be done scientifically. In this paper, we describe the r package mediation for conducting causal mediation analysis in applied empirical research.
The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. This function is used to plot results from the medsens function. Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal effects. In addition to the estimation of causal mediation effects, the software allows researchers to conduct sensitivity. The package requires little programming knowledge on the users side. Interest in mediation analysis has grown in many disciplines, such as the social sciences hoven and siegrist, 20.
This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. In other words, ade is the impact from the treatment on the outcome that does not go through the mediator. In the end i hope to con ve y that while a mediation analysis is an intuitiv e w ay to understand why a treatment w orks, it is. Oct 09, 2019 in addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. A mediation analysis is comprised of three sets of regression. Thus, causal mediation analysis has a potential to overcome the common criticism of quantitative social science research that it only provides a blackbox view of causality. Mediation model showing measured confounding of the exposure, mediator, and outcome. R package for causal mediation analysis 2014, journal of statistical software, 595, 8 with dustin tingley, kentaro hirose, luke keele and kosuke imai. We illustrate the use of the software with some of the empirical examples presented in imai et al. In observational studies, researchers often collect longitudinal data and face many statistical challenges, especially when investigating mediation. Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal e. Citeseerx chapter 8 causal mediation analysis using r. However, it is entirely possible, and very easy, to utilize this software without any understanding of the assumptions mediation analysis requires to yield a causal interpretation. Kosuke imai is assistant professor, department of politics.
In addition to the estimation of causal mediation effects, the software also allows researchers to. Sequential ignorability consists of two assumptions. Recently, imai, keele, and yamamoto 2010c and imai, keele, and tingley 2010b developed general algorithms to estimate causal mediation effects with the variety of data types that are often encountered in practice. Revisiting evidence from framing experiments 20, political analysis, 212. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. Imai, kosuke, luke keele, dustin tingley, and teppei yamamoto. Let m m 1, m p t, where m j is the jth mediatorconfounder. Recently, imai, keele, and yamamoto 2010c and imai, keele, and tingley 2010b developed. We implement parametric and non parametric mediation analysis. Figure1illustrates the core structure of the mediation package, which distinguishes between modelbased and designbased.
Research on identification of causal mechanisms via causal. Traditional approaches to mediation in the biomedical and social sciences are described. Causal mediation analysis is the study of mechanismsvariables measured. Analysis for causal mediation effects kosuke imai, luke keele and teppei yamamoto abstract. In many scientific disciplines, the goal of researchers is not only estimating. In addition to the estimation of causal mediation effects, the software. Causal mediation analysis, journal of statistical software, vol. In addition to the estimation of causal mediation effects, the software allows researchers to conduct sensitivity analysis for certain parametric models. Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. Apr 18, 2018 as mentioned earlier, the goal of mediation analysis is to decompose the total treatment effect into two parts.
As mentioned earlier, the goal of mediation analysis is to decompose the total treatment effect into two parts. The effect of the mediator is moderated by another variable. Given the importance of sequential ignorability, we argue that a mediation analysis is not complete without a sensitivity analysis. Our software, mediation, also allows users to conduct sensitivity analyses with only a single additional line of syntax, as illustrated in imai. A general approach to causal mediation analysis kosuke imai princeton university luke keele ohio state university dustin tingley harvard university traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We also make easytouse software available to implement the proposed methods. Z is the vector of other independent explanatory variables that are directly related with y, but not with x. The vertical axis lists indirect, direct, and total effects and the horizontal axis indicates the respective magnitudes. In many scientific disciplines, the goal of researchers is not only. An annotated resource list is provided, followed by a suggested article for a future epi 6 project relating to causal mediation. R package for causal mediation analysis kosuke imai. Mediation analysis from a counterfactual perspective with exposuremediator interaction can also be performed in r and stata using the macro provided by imai et al.
Causal mediation columbia university mailman school of. A general approach to causal mediation analysis kosuke imai. A common framework forthe statistical analysis of mechanisms has been mediation analysis,routinely conducted by applied researchers in a variety of disciplinesincluding epidemiology, political science, psychology, andsociology. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Recently, imai, keele, and yamamoto 2010c and imai. Introduction to mediation analysis university of virginia. A general approach to causal mediation analysis kosuke imai princeton university luke keele ohio state university dustin tingley harvard university traditionally in the social sciences, causal mediation. Tingley, dustin,teppei yamamoto, kentaro hirose, luke keele, and kosuke imai. Causal mediation analysis in observational studies we next consider causal mediation analysis in observational studies, which is the focus of pearls article. Moderated mediation tests the influence of a fourth or more variable on the mediated relationship between x and y. Aug 08, 2019 mediation analysis has been around a long time, though its popularity has varied between disciplines and over the years. N2 in this paper, we describe the r package mediation for conducting causal mediation analysis in applied empirical research. The easyto use software, mediation, is freely available at the comprehensive r. Causal mediation analysis for longitudinal data with.
Causal average mediation effects as well as average direct effects and proportions mediated for selected models can be plotted against two alternative sensitivity parameters. This package performs the methods and suggestions in imai, keele and yamamoto 2010, imai, keele and tingley 2010, imai, tingley and yamamoto 20, imai and yamamoto 20 and yamamoto 20. Identification, inference and sensitivity analysis for. It implements the methods and suggestions in imai, keele, and yamamoto 2010 and imai, keele, and tingley 2010. In this paper, we describe the r package mediation for. Department of data analysis ghent university software for mediation analysis two traditions traditional software for mediation analysis baron and kenny 1986 tradition many applied researchers still follow these steps using spsssas, often in combination with macrosscripts modern approach. The vertical axis lists indirect, direct, and total effects and the horizontal axis indicates the. This post will show examples using r, but you can use any statistical software. First is the scenario where researchers assume that both the treatment and potential mediators are asif randomized given possibly different sets of. This page briefly compares mediation analysis from both the traditional and causal inference frameworks. Abstract causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms.
Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. Dustin tingley, teppei yamamoto, kentaro hirose, luke keele, kosuke imai. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting iorw to estimate natural direct and indirect effects. Reply to commentary by imai, keele, tingley, and yamamoto. Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. Identification of causal mechanisms via causal mediation analysis. This package performs the methods and suggestions in imai, keele and yamamoto 2010, imai, keele and tingley 2010, imai. Plotting indirect, direct, and total effects from mediation analysis. While some fields have been attracted to the potential of mediation.
183 1080 915 389 471 271 863 631 176 83 1368 525 9 1437 1150 1285 258 1168 159 512 1395 1321 638 667 716 645 1431 1126 482 957 218 671 1229 1532 1338 1269 533 18 568 1078 1087 786 355 402 521 1233 778 1417 882