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Framework for evaluating user-specified finite stage policies and learning realistic policies via doubly robust loss functions. Policy learning methods include doubly robust Q-learning, sequential policy tree learning, and outcome weighted learning. See Nordland and Holst (2022) doi:10.48550/arXiv.2212.02335 for documentation and references.
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