# Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2112.08268 · HTML: https://mlsanity.com/paper/2112.08268/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 68 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)
- [Some methods for heterogeneous treatment effect estimation in high-dimensions](https://mlsanity.com/paper/1707.00102/) (2017)
- [Residual Weighted Learning for Estimating Individualized Treatment Rules](https://mlsanity.com/paper/1508.03179/) (2015)
- [Fairness in Ranking: A Survey](https://mlsanity.com/paper/2103.14000/) (2021)
- [Recursive Partitioning for Personalization using Observational Data](https://mlsanity.com/paper/1608.08925/) (2016)
- [Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation](https://mlsanity.com/paper/2106.09467/) (2021)
- [Satisficing in multi-armed bandit problems](https://mlsanity.com/paper/1512.07638/) (2015)
- [Constructive Preference Elicitation over Hybrid Combinatorial Spaces](https://mlsanity.com/paper/1711.07875/) (2017)
- [Preselection Bandits](https://mlsanity.com/paper/1907.06123/) (2019)
- [Learning Optimal Policies from Observational Data](https://mlsanity.com/paper/1802.08679/) (2018)

## Built on by (most cited first, 2 of 2)

- [Online Decision Mediation](https://mlsanity.com/paper/2310.18601/) (2023)
- [Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector](https://mlsanity.com/paper/2310.19091/) (2023)

## Abstract (arXiv)

Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the purpose of prediction (of ground-truth facts) to its use for prescriptive modeling. What is meant by this is the task of learning a model that stipulates appropriate decisions about the right course of action in real-world scenarios: Which medical therapy should be applied? Should this person be hired for the job? As argued in this article, prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. Therefore, to support the data-driven design of decision-making agents that act in a rational but at the same time responsible manner, a rigorous methodological foundation of prescriptive ML is needed. The purpose of this short paper is to elaborate on specific characteristics of prescriptive ML and to highlight some key challenges it implies. Besides, drawing connections to other branches of contemporary AI research, the grounding of prescriptive ML in a (generalized) decision-theoretic framework is advocated.