Causal mediation analysis with two mediators: A comprehensive guide to estimating total and natural effects across various multiple mediators setups.

Mediation analysis is widely used in psychology to assess how an independent variable transmits its causal effect on an outcome both directly and indirectly through intermediary variables known as mediators. Causal mediation analysis addresses numerous criticisms of product-of-coefficients approach, often regarded as the primary method for estimating indirect effects in psychological research. However, navigating causal mediation analysis, especially in settings with multiple mediators, can be challenging for those unfamiliar with its concepts, assumptions, and estimation strategies. In this tutorial, we therefore offer a comprehensive guide to conducting causal mediation analysis with two mediators across three data-generating mechanisms: setups with causally dependent mediators, independent mediators, and noncausally dependent mediators. For each of these mechanisms, we provide formal mathematical definitions and assumptions for the natural direct and indirect effects, along with less technical explanations of these concepts. We also provide R and Stata codes for estimating the natural direct effect, the joint natural indirect effect, and the path-specific natural indirect effects using four different estimators: the imputation approach, the extended imputation approach, the inverse probability weighted approach, and the extended quasi-Bayesian Monte Carlo approach. Additionally, we illustrate each of these methods with examples from the International Dating Violence Study. This tutorial aims to equip applied researchers in psychology with all the necessary tools to conduct causal mediation analysis involving two mediators across various multiple mediators setups. (PsycInfo Database Record (c) 2025 APA, all rights reserved).