Statistics Seminar

Abstract: The use of causal mediation analysis to evaluate the pathways by which an exposure affects an outcome is widespread in the social and biomedical sciences. Recent advances in this area have established formal conditions for identification and estimation of natural direct and indirect effects. However, these conditions typically involve stringent no unmeasured confounding assumptions and that the mediator has been measured without error. These assumptions may fail to hold in practice where mediation methods are often applied. I will give a detailed overview of the assumptions necessary for identification of indirect effects and describe estimators of indirect effects that are robust to forms of unmeasured confounding.