# Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings

arXiv: https://arxiv.org/abs/2010.15963 · HTML: https://mlsanity.com/paper/2010.15963/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [An Instrumental Variable Approach to Confounded Off-Policy Evaluation](https://mlsanity.com/paper/2212.14468/) (2022)
- [Safe Exploration for Efficient Policy Evaluation and Comparison](https://mlsanity.com/paper/2202.13234/) (2022)
- [Doubly Robust Alignment for Large Language Models](https://mlsanity.com/paper/2506.01183/) (2025)
- [Quasi-optimal Reinforcement Learning with Continuous Actions](https://mlsanity.com/paper/2301.08940/) (2023)
- [Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions](https://mlsanity.com/paper/2210.13373/) (2022)
- [Balanced Off-Policy Evaluation for Personalized Pricing](https://mlsanity.com/paper/2302.12736/) (2023)
- [A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets](https://mlsanity.com/paper/2202.10574/) (2022)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits](https://mlsanity.com/paper/2301.12357/) (2023)
- [SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP](https://mlsanity.com/paper/2406.02165/) (2024)
- [Doubly Robust Fusion of Many Treatments for Policy Learning](https://mlsanity.com/paper/2505.08092/) (2025)
- [Where to Intervene: Action Selection in Deep Reinforcement Learning](https://mlsanity.com/paper/2507.04187/) (2025)
- [Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects](https://mlsanity.com/paper/2609.33740/) (2026)
- [OTROPE: Optimal Transport-based Robust Off-policy Evaluation for Large Language Models](https://mlsanity.com/paper/2609.36264/) (2026)

## Abstract (arXiv)

We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete treatment settings. To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning. The key ingredient of our method lies in adaptively discretizing the treatment space using deep discretization, by leveraging deep learning and multi-scale change point detection. This allows us to apply existing OPE methods in discrete treatments to handle continuous treatments. Our method is further justified by theoretical results, simulations, and a real application to Warfarin Dosing.