AMT-MA redefines the meta-analysis target as a stable population effect via nuisance-anchor estimation and abstains from pooling under sign-flip heterogeneity using a precision-weighted diagnostic.
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Proposes a minimax-regret framework for learning generalizable CATE models from multisite data by minimizing worst-case regret over convex combinations of site-specific CATEs.
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Stable Transport Meta-Analysis for Heterogeneous Cardiovascular Trials: A Nuisance-Anchor Framework with a Sign-Stability Diagnostic
AMT-MA redefines the meta-analysis target as a stable population effect via nuisance-anchor estimation and abstains from pooling under sign-flip heterogeneity using a precision-weighted diagnostic.
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Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data
Proposes a minimax-regret framework for learning generalizable CATE models from multisite data by minimizing worst-case regret over convex combinations of site-specific CATEs.