Mediation analysis is an important tool for understanding causal mechanisms in epidemiology and social sciences. The estimation of direct and indirect effects with multiple mediators is a challenging problem. This thesis focused on the comparison of three mediation analysis methods with two sequential mediators. Our goal was to access the robustness of the methods in estimating natural indirect effect and partial indirect effect. In this thesis we simulated multiple scenarios based on a counterfactual framework and employed three weighted-marginal structural models to estimate direct and indirect effects (1-3). The bias, root mean squared error and 95% confidence interval coverage probability from the Monte-Carlo simulations were the criteria to compare the three methods. By comparing their performance in the estimation of direct and indirect effects, we concluded that the Lange method was more robust in mediation analysis with two sequential mediators compared with the methods by Steen and Hong.
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