A0228
Title: Causal mediation analysis for multilevel and functional data
Authors: Xi Luo - Univ of Texas Health Science Center at Houston (United States) [presenting]
Yi Zhao - Indiana University (United States)
Brian Caffo - Johns Hopkins University (United States)
Martin Lindquist - Johns Hopkins University (United States)
Michael Sobel - Columbia University (United States)
Abstract: Causal mediation analysis typically involves conditions that may not be applicable in neuroimaging studies. A multilevel causal mediation framework is introduced to overcome this limitation and more accurately quantify information flow in brain pathways. The framework is designed to tackle several challenges: unmeasured mediator outcome confounding, multilevel time series analysis, and the estimation of functional causal effects. The approach is grounded in multilevel structural equation modeling, complemented by relaxed likelihood estimation methods. Interestingly, certain causal estimates, typically unobtainable in simpler data structures, become identifiable in the more complex data setting. Proof of the asymptotic properties of the estimators is provided, and the numerical properties are illustrated through empirical analysis. Additionally, real fMRI data is utilized to demonstrate the practical effectiveness of the proposed framework.
