Title: Causal mediation analysis for generalized linear models
Abstract: In epidemiological, social science and other scientific studies, mediation analysis is often carried out to assert whether the effect of a treatment or an exposure on an outcome of interest is mediated by another covariate. This task concerns the underlying causal mechanism. In this talk, I will first present the counterfacual framework for causal inference, and provide background on causal mediation analysis while introducing the causal parameters of interest.
A common method for mediation analysis, termed "the difference method", compares estimates from models with and without the suspected mediator and results in estimates that can have a causal interpretation under certain assumptions. I will formulate the problem for generalized linear models, and consider the issue of having the same link function for the conditional and marginal models. Causal mediation effects will be then estimated by utilizing a data duplication algorithm, together with a generalized estimating equations approach that also provides straightforward variance estimation.
This is joint work with Xiaomei Liao and Donna Spiegelman