Introduces a transportability-based approach to model population-level exposure effects as a function of effect modifier prevalences for heterogeneity analysis.
Annual Review of Statistics and Its Application , volume = 10, number = 1, pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.
citing papers explorer
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From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity
Introduces a transportability-based approach to model population-level exposure effects as a function of effect modifier prevalences for heterogeneity analysis.
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Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.