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.

中文翻译：

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贝叶斯潜在结果中介分析教程

统计中介分析用于研究自变量和因变量之间关系的中间变量。中介分析的因果解释具有挑战性，因为将受试者随机化到自变量水平并不能排除中介因素与结果关系的未测量混杂因素的可能性。此外，用于中介分析的常用频率论方法计算给定零假设的数据概率，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，而贝叶斯框架中的统计中介则给出了间接效应的概率解释。本教程结合了因果推断和贝叶斯方法进行中介分析，因此间接和直接影响既有因果解释，也有概率解释。贝叶斯因果中介分析的步骤显示在对一个经验示例的应用中。