
Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) tools:::Rd_expr_doi("10.48550/arXiv.2212.02335") for documentation and references.
Maintainer: Andreas Nordland andreasnordland@gmail.com
Authors:
Klaus Holst klaus@holst.it (ORCID)
Useful links:
Report bugs at https://github.com/AndreasNordland/polle/issues