Learning Decisions Offline from Censored Observations with {\epsilon}-insensitive Operational Costs

TL;DR

This paper introduces a novel offline decision-making framework that handles censored data using { extepsilon}-insensitive costs, demonstrating improved performance and theoretical guarantees for ML models in managerial decision contexts.

Contribution

It proposes a new approach for offline decision-making with censored data using { extepsilon}-insensitive costs, along with theoretical bounds and empirical validation.

Findings

LR-{ extepsilon}NVC-R and NN-{ extepsilon}NVC outperform existing methods in cost savings.

Theoretical bounds confirm the stability and learnability of the proposed models.

Models produce order quantities closer to the optimal under known distributions.

Abstract

Many important managerial decisions are made based on censored observations. Making decisions without adequately handling the censoring leads to inferior outcomes. We investigate the data-driven decision-making problem with an offline dataset containing the feature data and the censored historical data of the variable of interest without the censoring indicators. Without assuming the underlying distribution, we design and leverage {\epsilon}-insensitive operational costs to deal with the unobserved censoring in an offline data-driven fashion. We demonstrate the customization of the {\epsilon}-insensitive operational costs for a newsvendor problem and use such costs to train two representative ML models, including linear regression (LR) models and neural networks (NNs). We derive tight generalization bounds for the custom LR model without regularization (LR-{\epsilon}NVC) and with regularization (LR-{\epsilon}NVC-R), and a high-probability generalization bound for the custom NN (NN-{\epsilon}NVC) trained by stochastic gradient descent. The theoretical results reveal the stability and learnability of LR-{\epsilon}NVC, LR-{\epsilon}NVC-R and NN-{\epsilon}NVC. We conduct extensive numerical experiments to compare LR-{\epsilon}NVC-R and NN-{\epsilon}NVC with two existing approaches, estimate-as-solution (EAS) and integrated estimation and optimization (IEO). The results show that LR-{\epsilon}NVC-R and NN-{\epsilon}NVC outperform both EAS and IEO, with maximum cost savings up to 14.40% and 12.21% compared to the lowest cost generated by the two existing approaches. In addition, LR-{\epsilon}NVC-R's and NN-{\epsilon}NVC's order quantities are statistically significantly closer to the optimal solutions should the underlying distribution be known.