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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.
citing papers explorer
-
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.
-
Mind the Gap: Optimal and Equitable Encouragement Policies
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.