Abstract
Designing 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Science Computing and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
