A Tutorial in Bayesian Potential Outcomes Mediation Analysis
Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibili...
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| Published in | Structural equation modeling Vol. 25; no. 1; pp. 121 - 136 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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United States
Routledge
01.01.2018
Psychology Press |
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| Online Access | Get full text |
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| Abstract | Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example. |
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| AbstractList | Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example. Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example. |
| Author | Gonzalez, Oscar Valente, Matthew J. Miočević, Milica MacKinnon, David P. |
| AuthorAffiliation | 2 Department of Psychology, Arizona State University 1 Department of Methodology and Statistics, Utrecht University |
| AuthorAffiliation_xml | – name: 1 Department of Methodology and Statistics, Utrecht University – name: 2 Department of Psychology, Arizona State University |
| Author_xml | – sequence: 1 givenname: Milica surname: Miočević fullname: Miočević, Milica email: m.miocevic@uu.nl organization: Utrecht University – sequence: 2 givenname: Oscar surname: Gonzalez fullname: Gonzalez, Oscar organization: Arizona State University – sequence: 3 givenname: Matthew J. surname: Valente fullname: Valente, Matthew J. organization: Arizona State University – sequence: 4 givenname: David P. surname: MacKinnon fullname: MacKinnon, David P. organization: Arizona State University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29910595$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Bayesian analysis Bayesian methods Bayesian Statistics causal inference Hypotheses mediation analysis potential outcomes Predictor Variables |
| Title | A Tutorial in Bayesian Potential Outcomes Mediation Analysis |
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