Doubly Flexible Estimation under Label Shift
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel estimation method for label shift problems, allowing for flexible modeling of both outcome and density ratios. The approach enhances data analysis when target populations have limited outcome data but share covariate distributions with source populations.
Area Of Science
- Statistics
- Machine Learning
- Biostatistics
Background
- Many studies require estimating parameters in a target population (Q) with partial data, using a source population (P) with complete data.
- Label shift, where the conditional distribution of covariates given the outcome is constant across populations, is a common scenario.
- Traditional methods often rely on accurate models for covariate-outcome relationships and outcome density ratios, which can be challenging to obtain.
Purpose Of The Study
- To develop a robust estimation procedure for target populations under label shift.
- To propose a method that is doubly flexible to misspecifications in outcome regression and density ratios.
- To address the difficulties in estimating outcome density ratios when outcome data is absent in the target population.
Main Methods
- Leveraging the label shift assumption: P(X|Y) is the same in populations P and Q.
- Utilizing standard nonparametric techniques to approximate conditional expectations of covariates given outcomes.
- Developing an estimation procedure that does not require explicit modeling of the outcome regression or density ratios.
Main Results
- The proposed method offers greater flexibility than doubly robust methods by allowing misspecification in both outcome regression and density ratios.
- The estimation procedure effectively utilizes information from the source population (P) to infer parameters in the target population (Q).
- Large sample theory for the proposed estimator is developed and validated through simulations and a real-world application.
Conclusions
- The novel estimation approach provides a powerful tool for handling label shift problems, particularly when direct estimation of density ratios is infeasible.
- The method's double flexibility to model misspecification enhances its applicability in diverse real-world scenarios, including clinical medicine and policy research.
- The study demonstrates the practical utility of the proposed method through its application to the MIMIC-III database.
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