Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
Panel Data Models with Grouped Factor Structure Under Unknown Group Membership , url =
2 Pith papers cite this work, alongside 138 external citations. Polarity classification is still indexing.
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A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
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Inference methods for unit-specific coefficients in panel data models with latent group structure
Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
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Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.