Heterogeneous Mediation Analysis for Causal Inference

• Activity Number: 8 - Recent Advances in Statistical Learning for High-Dimensional and Heterogeneous Complex Data
• Type: Invited
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Fei Xue and Xiwei Tang and Annie Qu*
• Companies: University of Pennsylvania and University of Virgina and University of Illinois at Urbana-Champaign
• Keywords: clustering analysis; multi-directional penalty; PTSD study; genetic data

Abstract

Mediation analysis is widely used to understand mediating mechanisms of variables in causal inference. However, existing approaches do not consider heterogeneity in mediation effects. Mediators in different sub-populations could have opposite effects on the outcome, and could be difficult to identify under the homogenous model framework. In this talk, we propose a new mediator selection method, which can identify sub-populations and select mediators in each sub-population for heterogenous data simultaneously. We perform a multi-directional clustering analysis to determine sub-group mediators and the corresponding subjects. Specifically, to select mediators, we propose a new joint penalty which penalizes the effect of independent variable on a mediator and the effect of a mediator on the response jointly. The proposed algorithm is implemented through the convex-smooth gradient descent. Our Numerical studies show that the proposed method outperforms the existing methods for heterogeneous data. We also apply the proposed method to a PTSD genetic African Americans data. This is joint work with Fei Xue and Xiwei Tang.