The analysis of imaging outcomes is a dynamic and rapidly evolving research field, driven by the growing accessibility of large-scale biomedical imaging databases. Imaging data, often characterized as functional data, presents unique opportunities and challenges for statistical analysis. Existing methods, however, are insufficient for handling the computational demands of large-scale medical imaging data or addressing issues such as unmeasured confounding and population heterogeneity in causal analysis. This research will develop advanced statistical tools to overcome these critical hurdles. By developing new techniques that efficiently process large-scale imaging information and provide more accurate causal insights, this work will advance national interests in scientific innovation and evidence-based decision-making. It will promote scientific progress in a vital area of imaging data analysis and aims to advance public health by enabling a deeper understanding of treatment effects from observational studies. The developed data analytics tools also have broad applicability across various fields, including aging research, digital health, and plant science, addressing challenges faced by modern society. Furthermore, the project will benefit the broader research community through the release of freely available software tools and will support STEM education by involving undergraduate and graduate students in hands-on research and integrating project findings into curriculum dev