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.

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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
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author Catarina Moreira
Ngoc Lan Chi Nguyen
Gilad Francis
Hadi Mohasel Afshar
Anna Lopatnikova
Sally Cripps
Roman Marchant
author_facet Catarina Moreira
Ngoc Lan Chi Nguyen
Gilad Francis
Hadi Mohasel Afshar
Anna Lopatnikova
Sally Cripps
Roman Marchant
author_sort Catarina Moreira
collection DOAJ
description 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.
format Article
id doaj-art-a79b2399ad054fed89f3c316ff486e4a
institution DOAJ
issn 2632-3249
language English
publishDate 2025-01-01
publisher Cambridge University Press
record_format Article
series Data & Policy
spelling doaj-art-a79b2399ad054fed89f3c316ff486e4a2025-08-20T03:15:55ZengCambridge University PressData & Policy2632-32492025-01-01710.1017/dap.2024.93Bayesian causal discovery for policy decision makingCatarina Moreira0https://orcid.org/0000-0002-8826-5163Ngoc Lan Chi Nguyen1https://orcid.org/0000-0003-4349-8024Gilad Francis2https://orcid.org/0000-0001-7910-8556Hadi Mohasel Afshar3Anna Lopatnikova4Sally Cripps5https://orcid.org/0000-0003-3207-172XRoman Marchant6https://orcid.org/0000-0002-0969-2352Human Technology Institute, University Technology Sydney, Ultimo, NSW, AustraliaSchool of Computer Science, The University of Sydney, Sydney, NSW, AustraliaHuman Technology Institute, University Technology Sydney, Ultimo, NSW, AustraliaHuman Technology Institute, University Technology Sydney, Ultimo, NSW, AustraliaHuman Technology Institute, University Technology Sydney, Ultimo, NSW, Australia Discipline of Business Analytics, The University of Sydney, Darlington, NSW, AustraliaHuman Technology Institute, University Technology Sydney, Ultimo, NSW, AustraliaHuman Technology Institute, University Technology Sydney, Ultimo, NSW, AustraliaThis 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.https://www.cambridge.org/core/product/identifier/S2632324924000932/type/journal_articleBayesian networkscausal discoverycausal effect estimation
spellingShingle Catarina Moreira
Ngoc Lan Chi Nguyen
Gilad Francis
Hadi Mohasel Afshar
Anna Lopatnikova
Sally Cripps
Roman Marchant
Bayesian causal discovery for policy decision making
Data & Policy
Bayesian networks
causal discovery
causal effect estimation
title Bayesian causal discovery for policy decision making
title_full Bayesian causal discovery for policy decision making
title_fullStr Bayesian causal discovery for policy decision making
title_full_unstemmed Bayesian causal discovery for policy decision making
title_short Bayesian causal discovery for policy decision making
title_sort bayesian causal discovery for policy decision making
topic Bayesian networks
causal discovery
causal effect estimation
url https://www.cambridge.org/core/product/identifier/S2632324924000932/type/journal_article
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AT hadimohaselafshar bayesiancausaldiscoveryforpolicydecisionmaking
AT annalopatnikova bayesiancausaldiscoveryforpolicydecisionmaking
AT sallycripps bayesiancausaldiscoveryforpolicydecisionmaking
AT romanmarchant bayesiancausaldiscoveryforpolicydecisionmaking