NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
Asymptotics for statistical treatment rules,
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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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Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
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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.