A0626
Title: Causal inference in biomedical imaging via functional linear structural equation models
Authors: Ting Li - Shanghai University of Finance and Economics (China) [presenting]
Abstract: Understanding the causal effects of organ-specific features from medical imaging on clinical outcomes is essential for biomedical research and patient care. A novel functional linear structural equation model (FLSEM) is proposed to capture the relationships among clinical outcomes, functional imaging exposures, and scalar covariates like genetics, sex, and age. Traditional methods struggle with the infinite-dimensional nature of exposures and complex covariates. FLSEM overcomes these challenges by establishing identifiable conditions using scalar instrumental variables. The functional group support detection and root finding (FGS- DAR) algorithm is developed for efficient variable selection, supported by rigorous theoretical guarantees, including selection consistency and accurate parameter estimation. A test statistic is further proposed to test the nullity of the functional coefficient, establishing its null limit distribution. The approach is validated through extensive simulations and applied to UK Biobank data, demonstrating robust performance in detecting causal relationships from medical imaging.
