Miočević Milica, Gonzalez Oscar, Valente Matthew J, MacKinnon David P
Department of Methodology and Statistics, Utrecht University.
Department of Psychology, Arizona State University.
Struct Equ Modeling. 2018;25(1):121-136. doi: 10.1080/10705511.2017.1342541. Epub 2017 Jul 25.
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.
统计中介分析用于研究自变量和因变量之间的中间变量。中介分析的因果解释具有挑战性，因为将受试者随机分配到自变量的不同水平并不能排除中介变量与结果关系中未测量混杂因素的可能性。此外，常用的频率主义中介分析方法计算的是在零假设下数据的概率，这与贝叶斯分析中给定数据的假设概率不同。在某些假设下，将潜在结果框架应用于中介分析可以计算因果效应，并且贝叶斯框架中的统计中介给出了间接效应的概率解释。本教程将因果推断和贝叶斯方法结合用于中介分析，以便间接效应和直接效应都具有因果和概率解释。贝叶斯因果中介分析的步骤在一个实证例子的应用中展示。
