# Applications of 0-1 Neural Networks in Prescription and Prediction

arXiv: https://arxiv.org/abs/2402.18851 · HTML: https://mlsanity.com/paper/2402.18851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 77 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Solving Mixed Integer Programs Using Neural Networks](https://mlsanity.com/paper/2012.13349/) (2020)
- [Bounding and Counting Linear Regions of Deep Neural Networks](https://mlsanity.com/paper/1711.02114/) (2017)
- [Strong mixed-integer programming formulations for trained neural networks](https://mlsanity.com/paper/1811.08359/) (2018)
- [On Interpretability of Artificial Neural Networks: A Survey](https://mlsanity.com/paper/2001.02522/) (2020)
- [Some methods for heterogeneous treatment effect estimation in high-dimensions](https://mlsanity.com/paper/1707.00102/) (2017)
- [Recursive Partitioning for Personalization using Observational Data](https://mlsanity.com/paper/1608.08925/) (2016)
- [Strong Optimal Classification Trees](https://mlsanity.com/paper/2103.15965/) (2021)
- [Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study](https://mlsanity.com/paper/1712.06174/) (2017)
- [Non-Stationary Bandits with Habituation and Recovery Dynamics](https://mlsanity.com/paper/1707.08423/) (2017)
- [Optimal Policy Trees](https://mlsanity.com/paper/2012.02279/) (2020)
- [Learning Optimal Prescriptive Trees from Observational Data](https://mlsanity.com/paper/2108.13628/) (2021)
- [Behavioral Analytics for Myopic Agents](https://mlsanity.com/paper/1702.05496/) (2017)
- [Optimal training of integer-valued neural networks with mixed integer programming](https://mlsanity.com/paper/2009.03825/) (2020)
- [Model Based Reinforcement Learning for Personalized Heparin Dosing](https://mlsanity.com/paper/2304.10000/) (2023)
- [A Mixed-Integer Programming Approach to Training Dense Neural Networks](https://mlsanity.com/paper/2201.00723/) (2022)

## Abstract (arXiv)

A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this, we introduce prescriptive networks (PNNs), shallow 0-1 neural networks trained with mixed integer programming that can be used with counterfactual estimation to optimize policies in medium data settings. These models offer greater interpretability than deep neural networks and can encode more complex policies than common models such as decision trees. We show that PNNs can outperform existing methods in both synthetic data experiments and in a case study of assigning treatments for postpartum hypertension. In particular, PNNs are shown to produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02) over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best prescriptive modeling technique. Moreover PNNs were more likely than all other models to correctly identify clinically significant features while existing models relied on potentially dangerous features such as patient insurance information and race that could lead to bias in treatment.