We consider two small-sample prediction tasks that leverage real-world datasets with known confounders: prediction of drug sensitivity in breast cancer cell lines, which is confounded by subtype (clinical, i.e., hormone-receptor positive [HR+], HER2 amplified, triple negative; and molecular, i.e., luminal, basal); and prediction of Alzheimer’s disease (AD) severity in postmortem brain specimens, which is confounded by an individual’s chronological age. This evidence concerns the gene ERBB2 and Alzheimer disease.