# WiseR: An end-to-end structure learning and deployment framework for causal graphical models

arXiv: https://arxiv.org/abs/2108.07046 · HTML: https://mlsanity.com/paper/2108.07046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package](https://mlsanity.com/paper/1406.7648/) (2014)
- [Learning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in India](https://mlsanity.com/paper/2012.01930/) (2020)
- [Learning to Address Health Inequality in the United States with a Bayesian Decision Network](https://mlsanity.com/paper/1809.09215/) (2018)

## Abstract (arXiv)

Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data. We present wiseR, an open source application for learning, evaluating and deploying robust causal graphical models using graph neural networks and Bayesian networks. We demonstrate the utility of this application through application on for biomarker discovery in a COVID-19 clinical dataset.