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Found 5 papers, 1 papers with code
Minimax Regret Learning for Data with Heterogeneous Subgroups
Given the observed groups of data, we develop a min-max-regret (MMR) learning framework for general supervised learning, which targets to minimize the worst-group regret.
PASTA: Pessimistic Assortment Optimization
The objective is to use the offline dataset to find an optimal assortment.
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
However, when the growth of testing sample size available for training is in a slower order, efficient value function estimates may not perform well anymore.
Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models
Other than potential misspecified nuisance models, most existing methods do not account for the potential problem when the variance of outcome is heterogeneous among covariates and treatment.
Learning Optimal Distributionally Robust Individualized Treatment Rules
We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are "close" to the training distribution.
