A0406
Title: High-dimensional causal mediation analysis
Authors: Yeying Zhu - University of Waterloo (Canada) [presenting]
Abstract: Causal mediation analysis has become popular in recent years, in which researchers not only aim to estimate the causal effect of a treatment, but also try to understand how the treatment affects the outcome through intermediate variables, namely mediators. We propose a set of generalized structural equations to estimate the direct and indirect effects for mediation analysis when the number of mediators is of high dimensionality. Specifically, a two-step procedure is considered where the penalization framework can be adopted to perform variable selection. The obtained estimators can be interpreted as causal effects without imposing the linear assumption on the model structure. The performance of Sobel's method in obtaining the standard error and confidence interval for the estimated joint indirect effect is also evaluated in simulation studies. The proposed method is applied to investigate how DNA methylation plays a role in the regulation of human stress reactivity impacted by childhood trauma.
