The Department of Biostatistics Presents: Yi Zhao, John Hopkins Bloomberg School of Public Health

Yi Zhao

Department of Biostatistics

John Hopkins Bloomberg School of Public Health

Causal Mediation Analysis with High-Dimensional Mediators

In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate
the pathway effects, commonly expressed as products of coefficients. However, it becomes unstable to fit such models with high-dimensional mediators, especially for a general setting where all the mediators are causally dependent and the exact causal relationships between them are unknown. This study proposes a sparse mediation model using a regularized SEM approach, where sparsity means that a
small number of mediators have nonzero mediation effects between a treatment and an outcome. To address the model selection challenge, we innovate by introducing a new penalty called Pathway Lasso. This penalty function is a convex relaxation of
the non-convex product function, and it enables a computationally tractable optimization criterion to estimate and select many pathway effects simultaneously. We develop a fast ADMM-type algorithm to compute the model parameters, and we show that the iterative updates can be expressed in closed form. Theoretical analysis demonstrates that our method is able to consistently estimate the mediation pathway effects. On both simulated data and a real fMRI dataset, the proposed approach yields higher pathway selection accuracy and lower estimation bias than other competing methods.

Light refreshments for seminar guests are served in 1690 SPH I at 3:10pm