Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum

TL;DR

This paper develops a method to estimate causal effects in Alzheimer's research despite unobserved confounders by using a probabilistic latent factor model to identify substitute confounders, improving causal inference from observational neuroimaging data.

Contribution

It introduces a novel approach leveraging dependencies among causes to identify causal effects without measuring all confounders, validated through theoretical proofs and empirical data.

Findings

The method enables causal effect identifiability with unmeasured confounders.

It successfully uncovers important causes in Alzheimer's data.

The approach outperforms traditional methods in semi-synthetic experiments.

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.