Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders


Introduction: Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer’s disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic.

Methods: We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders.

Results: Our analyses of N=732 subjects from the Alzheimer’s Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD.

Discussion: The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.

Alzheimer’s & Dementia
Sebastian Pölsterl
AI Researcher

My research interests include machine learning for time-to-event analysis, causal inference and biomedical applications.