RewardEstimation Documentation

RewardEstimation provides functionality for estimating rewards for use in prescriptive problems. This documentation includes:

• an overview of reward estimation, including an introduction to observational data and the difficulty of evaluating prescriptive problems, and how reward estimation can assist with this task
• conceptual guides to understand the theory behind the reward estimation process for both categorical treatments and numeric treatments
• details of the various learners for reward estimation, including descriptions of the available parameters
• tips, tricks and best practices for reward estimation
• information on advanced topics
• the RewardEstimation API reference

For more information and a demonstration of reward estimation applied to real problems, you can also refer to the following examples:

• Categorical treatment:
  • Quick Start Guide: Optimal Prescriptive Trees
  • Quick Start Guide: Optimal Policy Trees
• Single numeric treatment:
  • Quick Start Guide: Optimal Policy Trees
  • Case Study: Grocery Pricing
• Multiple numeric treatments:
  • Case Study: Diabetes Management Policy