### 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.

Original language | English (US) |
---|---|

Pages (from-to) | 1-16 |

Number of pages | 16 |

Journal | Structural Equation Modeling |

DOIs | |

State | Accepted/In press - Jul 23 2017 |

### Keywords

- Bayesian methods
- causal inference
- mediation analysis
- potential outcomes

### ASJC Scopus subject areas

- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)

## Fingerprint Dive into the research topics of 'A Tutorial in Bayesian Potential Outcomes Mediation Analysis'. Together they form a unique fingerprint.

## Cite this

*Structural Equation Modeling*, 1-16. https://doi.org/10.1080/10705511.2017.1342541