Abstract
Studying the relationship between neuroanatomy and cognitive decline due to
Alzheimer’s has been a major research focus in the last decade. However, to
infer cause-effect relationships rather than simple associations from
observational data, we need to (i) express the causal relationships leading to
cognitive decline in a graphical model, and (ii) ensure the causal effect of
interest is identifiable from the collected data. We derive a causal graph from
the current clinical knowledge on cause and effect in the Alzheimer’s disease
continuum, and show that identifiability of the causal effect requires all
confounders to be known and measured. However, in complex neuroimaging studies,
we neither know all potential confounders nor do we have data on them. To
alleviate this requirement, we leverage the dependencies among multiple causes
by deriving a substitute confounder via a probabilistic latent factor model. In
our theoretical analysis, we prove that using the substitute confounder enables
identifiability of the causal effect of neuroanatomy on cognition. We
quantitatively evaluate the effectiveness of our approach on semi-synthetic
data, where we know the true causal effects, and illustrate its use on real
data on the Alzheimer’s disease continuum, where it reveals important causes
that otherwise would have been missed.
Publication
International Conference on Information Processing in Medical Imaging