Exact parametric causal mediation analysis for a binary outcome with a binary mediator

Abstract

A parametric expression for causal natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator. The proposed effect decomposition does not require the outcome to be rare and generalizes the existing ones, allowing for interactions between both the exposure and the mediator and confounding covariates. Further, it outlines the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, in parallel with results derived under the rare outcome assumption. Formulae for standard errors, obtained via the delta method, are also given. A simulation study is implemented which compares these estimators to a number of competing ones. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated as an example.

arXiv comment24 pages, 5 figures

References in corpus (5)

• Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
• Assumptions of IV Methods for Observational Epidemiology
• Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
• On marginal and conditional parameters in logistic regression models
• Graphical models for mediation analysis

Cited by in corpus (5)

• A causal approach to functional mediation analysis with application to a smoking cessation intervention
• Mediation analysis with case-control sampling: Identification and estimation in the presence of a binary mediator
• Exact mediation analysis for ordinal outcome and binary mediator
• Interval identification of natural effects in the presence of outcome-related unmeasured confounding
• Path Analysis for Binary Random Variables