Reproduce “Insurance Pricing Optimization via Off-Policy Evaluation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Sascha Günther, Dimitri Semenovich, Mario V. Wüthrich
A framework is developed for optimizing insurance pricing by treating it as an off-policy control problem, introducing a kernelized Inverse Propensity Score (K-IPS) estimator that significantly reduces variance and allows for evaluating policies on new action spaces, ultimately yielding more robust policy optimization compared to predict-then-optimize methods.
Key insights
- Off-policy evaluation offers a robust framework for assessing and optimizing insurance pricing policies without requiring continuous, costly real-world experimentation.
- Exploiting local structure in the action space through kernelization significantly reduces the variance of off-policy estimators, leading to more reliable policy value estimates.
- The choice of kernel matrix, particularly the variance-optimal one, can substantially improve estimation accuracy, while simpler kernel constructions can offer comparable empirical performance in certain settings.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.