aceMachine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review
Machine learning (ML) informs nursing practice, but prediction cannot establish intervention or workflow effects. Aim To map ML use within causal effect estimation and causal evaluations of ML-enabled interventions in nursing and midwifery. Eight electronic sources were searched for reports published January 2015-June 2026, followed by backward citation searching. Eligibility required a causal estimand, corresponding identification strategy, substantive ML role, and nursing or midwifery relevance. Two reviewers…
Among ML-enabled nursing interventions or workflows evaluated causally, studies reported favorable effects on mortality, care escalation, message-review time, and nursing processes.
Several controlled estimates for ML-enabled nursing interventions were null or imprecise, and the evidence base is small and methodologically uneven.
The evidence base is small and methodologically uneven, and several controlled estimates were null or imprecise, so the favorable results are not conclusive.
Evidence
- Peer-reviewedNursing & Health Sciences2026-12-01
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Truvace Impact Record TRV-2026-1339, v1: “Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.” Truvace, 2026-10-14. /record/TRV-2026-1339 (accessed at citation time). sha256 9b286124e64c55e1…
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