# Optimization over Continuous and Multi-dimensional Decisions with Observational Data

arXiv: https://arxiv.org/abs/1807.04183 · HTML: https://mlsanity.com/paper/1807.04183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

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

- [Distributionally Robust Learning](https://mlsanity.com/paper/2108.08993/) (2021)
- [Invariant Policy Learning: A Causal Perspective](https://mlsanity.com/paper/2106.00808/) (2021)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [Contextual Stochastic Vehicle Routing with Time Windows](https://mlsanity.com/paper/2402.06968/) (2024)
- [Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance](https://mlsanity.com/paper/2604.14370/) (2026)

## Abstract (arXiv)

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets.