Comparisons of Variable Selection and Inference Methods in High-dimensional Mediation Analysis

Music City Center 

Mediation analysis is a framework to understand how a treatment affects the outcome through intermediate variables, namely mediators. Over the past decades, large and high-dimensional datasets have become easily stored and publicly available. This has led to many recent advances in mediation analysis, including developing models to fit more complex data structures and methods for mediator selections in high-dimensional settings. The statistical inference procedure following the mediator selection is also an important step in the mediation analysis. We study the effect of different variable selection and inference procedures through simulation studies. In this talk, I will discuss our simulation settings and the findings to provide guidelines that help distinguish among various approaches, highlight the advantages and disadvantages of each, and identify ones that perform better in certain scenarios.

Linear structural equation modeling

Penalization

Bootstrap 

Isolated Statisticians