We address the challenges posed by heteroscedastic noise in contextual decision-making. We propose a consistent Shrinking Neighborhood Estimation (SNE) technique that successfully estimates contextual performance under unpredictable variances. Furthermore, we propose a Rate-Efficient Sampling rule designed to enhance the performance of the SNE. The effectiveness of the combined solution ``Contextual Optimizer through Neighborhood Estimation"(CONE) is validated through theorems and numerical benchmarking. The methodologies have been further deployed to address a staffing challenge in a hospital call center, exemplifying their substantial impact and practical utility in real-world scenarios.
@article{arxiv.2308.10235,
title = {Contextual Optimizer through Neighborhood Estimation for prescriptive analysis},
author = {Xiao Jin and Yichi Shen and Loo Hay Lee and Christine A. Shoemaker},
journal= {arXiv preprint arXiv:2308.10235},
year = {2023}
}