causal inference

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 …

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

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 …

Detect and Correct Bias in Multi-Site Neuroimaging Datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from …

Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference

Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex machine learning models. A potential solution is to increase sample size …