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Causal ABMs: Learning Plausible Causal Models using Agent-based Modeling

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dc.contributor.authorValogianni, Konstantina
dc.contributor.authorPadmanabhan, Balaji
dc.contributor.authorQiu, Liangfei
dc.contributor.rorhttps://ror.org/02jjdwm75
dc.date.accessioned2024-11-08T14:51:47Z
dc.date.available2024-11-08T14:51:47Z
dc.date.issued2022-08-15
dc.description.abstractWe present Causal ABM, a methodology to derive causal structures describing complex underlying behavioral phenomena. Agent-based models (ABMs) have powerful advantages for causal modeling that have not been explored sufficiently. Unlike traditional causal estimation approaches which often result in “one best” causal structure that is learned, two properties of ABMs - equifinality (the ability of different sets of conditions or model representations to yield the same outcome) and mutlifinality (the same ABM might yield different outcomes) - can be exploited to learn multiple diverse “plausible causal models” from data. Using an illustrative example of news sharing on social networks we show how this idea can be applied to learn such causal sets. We also show how genetic algorithms can be used as a estimation technique to learn multiple plausible causal models from data due to their parallel search structure. However, significant computational challenges remain before this can be generally applied, and we, therefore, highlight specific key issues that need to be addressed in future work.
dc.description.peerreviewedyes
dc.description.statusPublished
dc.formatapplication/pdf
dc.identifier.citationValogianni, Konstantina and Padmanabhan, Balaji and Qiu, Liangfei, Causal ABM: A Methodology for Learning Plausible Causal Models using Agent-Based Modeling (January 31, 2023). http://dx.doi.org/10.2139/ssrn.4343647
dc.identifier.doihttp://dx.doi.org/10.2139/ssrn.4343647
dc.identifier.issn2640-3498
dc.identifier.urihttps://hdl.handle.net/20.500.14417/3325
dc.journal.titleProceedings of Machine Learning Research
dc.language.isoen
dc.page.final29
dc.page.initial3
dc.page.total26
dc.publisherSSRN
dc.relation.departmentInformation Systems & Technology
dc.relation.entityIE University
dc.relation.schoolIE Business School
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordCausal inference
dc.subject.keyword Agent-based models
dc.subject.keywordEquifinality
dc.subject.keywordMutlifinality
dc.titleCausal ABMs: Learning Plausible Causal Models using Agent-based Modeling
dc.typeinfo:eu-repo/semantics/article
dc.version.typeinfo:eu-repo/semantics/acceptedVersion
dc.volume.number185
dspace.entity.typePublication
relation.isAuthorOfPublication176a05d2-7442-4e6e-a6da-dcf2f9063787
relation.isAuthorOfPublication.latestForDiscovery176a05d2-7442-4e6e-a6da-dcf2f9063787
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