Application of Causal Inference in Large-Scale Biomedical Data

Abstract

This dissertation contains three projects that tackle the challenges in the application of causal inference on large-scale biomedical data. Project 1 proposes a novel mediation analysis framework with the existence of multiple mediators and outcomes. It can extract the mediation pathway efficiently and estimate the mediation effect from multiple mediators simultaneously. The effectiveness of the proposed method is validated through extensive simulation and a real data application focusing on human connectome study. Project 2 introduces a doubly machine learning based method, assisted by algorithm ensemble, for estimating longitudinal causal effects. This approach reduces estimation bias and accommodates high-dimensional covariates. The validity of the proposed method is justified by simulation studies and an application to adolescent brain cognitive development data, specifically evaluating the impact from sleep insufficiency on youth cognitive development. Project 3 develops a new bias-reduction estimation that addresses unmeasured confounding by leveraging proximal learning and negative control outcome techniques. This method can handle a moderate number of exposures and multivariate outcomes in the presence of unmeasured confounders. Both numerical experiment and data application using UK Biobank demonstrate that the proposed method effectively reduces estimation bias caused by unmeasured confounding. Collectively, these three projects introduce innovative methodologies for causal inference in neuroimaging, advancing mediation analysis in neuroimaging, improving longitudinal causal effect estimation, and reducing estimation bias in the presence of unmeasured confounding.