9/20/2023 0 Comments Mediator 9 tutorialThis goal is accomplished using statistical mediation 1 analysis ( Baron & Kenny, 1986 Judd & Kenny, 1981 MacKinnon, 2008). The central goal of many research areas is to study intermediate variables, known as mediators, through which an independent variable affects a dependent variable. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. 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. 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. 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. Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables.
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