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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.
Package details |
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Author | Andreas Nordland [aut, cre], Klaus Holst [aut] (<https://orcid.org/0000-0002-1364-6789>) |
Maintainer | Andreas Nordland <andreasnordland@gmail.com> |
License | Apache License (>= 2) |
Version | 1.4 |
Package repository | View on CRAN |
Installation |
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