Design and validation of structural causal model: a focus on SENSE-EGRA datasets

Simple item page
dc.contributor.authorAyem, Gabriel Terna
dc.contributor.authorNsang, Augustine Shey
dc.contributor.authorIgoche, Bernard Igoche
dc.contributor.authorNaankang, Garba
dc.date.accessioned2024-06-26T14:15:18Z
dc.date.available2024-06-26T14:15:18Z
dc.date.issued2023-12-15
dc.description.abstractDesigning and validation of causal model correctness from a dataset whose background knowledge is gotten from a research process is not a common phenomenon. In fact, studies have shown that in many critical areas such as healthcare and education, researchers develop models from direct acyclic graphs without testing them. This phenomenon is worrisome and is bound to cast a dark shadow on the inference estimates that many arise from such models. In this study, we have design a novel application-based SCM for the first time using the background knowledge gotten from the American university of Nigeria (AUN), Yola, on the letter identification subtask of Early Grade reading Assessment (EGRA) program on Strengthen Education in Northeast Nigeria (SENSE-EGRA) project dataset, which was sponsored by the USAID. We employed the conditional independence test (CIT) criteria for the model’s correctness validation testing, and the results shows a near perfect SCM.en_UK
dc.identifier.citationAyem GT, Nsang AS, IgocheBI, Naankang G. (2023). Design and validation of structural causal model: a focus on SENSE-EGRA datasets. International Journal of Advanced Science Computing and Engineering, Volume 5, Issue 3, December 2023, pp. 257–268en_UK
dc.identifier.issn2714-7533
dc.identifier.urihttps://doi.org/10.62527/ijasce.5.3.177
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22566
dc.language.isoen_UKen_UK
dc.publisherSociety of Visual Informatics (SOTVI)en_UK
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectCausalityen_UK
dc.subjectstructural causal validationen_UK
dc.subjectmodel assumptionen_UK
dc.subjectobservational datasetsen_UK
dc.subjectmodel testingen_UK
dc.titleDesign and validation of structural causal model: a focus on SENSE-EGRA datasetsen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-10-15

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Design_and_validation_of_structural_causal_model-2024.pdf
Size:
665.5 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)