Ayem, Gabriel TernaNsang, Augustine SheyIgoche, Bernard IgocheNaankang, Garba2024-06-262024-06-262023-12-15Ayem 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. 257–2682714-7533https://doi.org/10.62527/ijasce.5.3.177https://dspace.lib.cranfield.ac.uk/handle/1826/22566Designing 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-UKAttribution-ShareAlike 4.0 InternationalCausalitystructural causal validationmodel assumptionobservational datasetsmodel testingArticle