Bayesian causal discovery for policy decision making
This paper demonstrates how learning the structure of a Bayesian network, often used to predict and represent causal pathways, can be used to inform policy decision-making.
Saved in:
| Main Authors: | Catarina Moreira, Ngoc Lan Chi Nguyen, Gilad Francis, Hadi Mohasel Afshar, Anna Lopatnikova, Sally Cripps, Roman Marchant |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Data & Policy |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632324924000932/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools
by: Francesco Canonaco, et al.
Published: (2025-08-01) -
Bayesian adaptive trials for social policy
by: Sally Cripps, et al.
Published: (2025-01-01) -
Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models
by: Mi Jin Noh, et al.
Published: (2025-01-01) -
CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series
by: Luca Castri, et al.
Published: (2024-12-01) -
CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy
by: Zahra Taghados, et al.
Published: (2025-04-01)