# Balanced Off-Policy Evaluation for Personalized Pricing

arXiv: https://arxiv.org/abs/2302.12736 · HTML: https://mlsanity.com/paper/2302.12736/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile](https://mlsanity.com/paper/1807.02629/) (2018)
- [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://mlsanity.com/paper/1612.01205/) (2016)
- [Off-policy Bandits with Deficient Support](https://mlsanity.com/paper/2006.09438/) (2020)
- [Policy Evaluation and Optimization with Continuous Treatments](https://mlsanity.com/paper/1802.06037/) (2018)
- [Balanced Policy Evaluation and Learning](https://mlsanity.com/paper/1705.07384/) (2017)
- [Semi-Parametric Efficient Policy Learning with Continuous Actions](https://mlsanity.com/paper/1905.10116/) (2019)
- [More Efficient Policy Learning via Optimal Retargeting](https://mlsanity.com/paper/1906.08611/) (2019)
- [Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings](https://mlsanity.com/paper/2010.15963/) (2020)
- [Balanced off-policy evaluation in general action spaces](https://mlsanity.com/paper/1906.03694/) (2019)
- [Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing](https://mlsanity.com/paper/2003.07017/) (2020)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [Comment: Entropy Learning for Dynamic Treatment Regimes](https://mlsanity.com/paper/2004.02778/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [The Bandit Whisperer: Communication Learning for Restless Bandits](https://mlsanity.com/paper/2408.05686/) (2024)
- [Confounding-Robust Policy Improvement with Human-AI Teams](https://mlsanity.com/paper/2310.08824/) (2023)
- [Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning](https://mlsanity.com/paper/2308.14897/) (2023)
- [The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions](https://mlsanity.com/paper/2609.23374/) (2026)

## Abstract (arXiv)

We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand. The goal is to perform off-policy evaluation for a new personalized pricing policy that maps features to prices. Methods based on inverse propensity weighting (including doubly robust methods) for off-policy evaluation may perform poorly when the logging policy has little exploration or is deterministic, which is common in pricing applications. Building on the balanced policy evaluation framework of Kallus (2018), we propose a new approach tailored to pricing applications. The key idea is to compute an estimate that minimizes the worst-case mean squared error or maximizes a worst-case lower bound on policy performance, where in both cases the worst-case is taken with respect to a set of possible revenue functions. We establish theoretical convergence guarantees and empirically demonstrate the advantage of our approach using a real-world pricing dataset.