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However, causal effect analyses in such populations are often challenged by limited sample sizes, leading to biased or unstable estimates when only target-population data are used. Existing transfer causal learning methods borrow information from a larger related source domain, but may be unstable when source-domain estimation is high-dimensional or when irrelevant covariates introduce noise into transferred nuisance models. We propose a double shrinkage transfer causal learning (DSTCL) estimator that regularizes both the initial source domain estimation and the source-target parameter differences, and then estimates causal effects using a doubly robust framework. We evaluated DSTCL in simulations under varying source sample sizes, inter-domain similarities, covariate dimensions, and other settings, and then applied it to the Alzheimer\u2019s Disease Neuroimaging Initiative dataset using African American participants as the target domain and non-Hispanic White participants as the source domain. In simulations, DSTCL achieved small bias, low mean squared error, and stable confidence interval coverage, generally outperforming competing methods. In the ADNI application, lower baseline ABeta42, reflecting greater amyloid burden, was associated with worse 12-month cognitive performance among African American participants, with DSTCL producing the narrowest confidence interval among the methods compared. These findings suggest that DSTCL provides a practical framework for transfer causal inference in biomedical studies with limited target population.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014706","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T17:44:11Z","timestamp":1787593451000},"page":"e1014706","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Double shrinkage transfer causal learning: An application to alzheimer\u2019s 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