Title: Causal mediation analysis: From simple to more robust estimation strategies
Authors: Trang Nguyen - Johns Hopkins Bloomberg School of Public Health (United States) [presenting]
Abstract: The aim is to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and regression-based prediction) and a simple way of combining them (weighted models) and show how we can generate a range of estimators with different modeling requirements and robustness properties. The primary goal is to help build an intuitive appreciation for robust estimation that is conducive to sound practice. A second goal is to provide a menu of estimators that practitioners can choose from for the estimation of marginal natural (in)direct effects. The estimators generated from this exercise include some that coincide or are similar to existing estimators and others that have not previously appeared in the literature. We note several different ways to estimate the weights for cross-world weighting based on three expressions of the weighting function, including one that is novel; and show how to check the resulting covariate and mediator balance. We use a random continuous weights bootstrap to obtain confidence intervals, and also derive general asymptotic (sandwich) variance formulas for the estimators. The estimators are illustrated using data from an adolescent alcohol use prevention study.