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All Journal International Journal of Advanced Science Computing and Engineering Journal of Computing Theories and Applications
Ayem, Gabriel Terna
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Design and Validation of Structural Causal Model: A Focus on EGRA Dataset Ayem, Gabriel Terna; Asilkan, Ozcan; Iorliam, Aamo
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9304

Abstract

Designing and validating structural causal model (SCM) correctness from a dataset whose background knowledge is obtained from a research process is not a common phenomenon. Studies have shown that in many critical areas, such as healthcare and education, researchers develop models from direct acyclic graphs (DAG), a component of an SCM, without testing them. This phenomenon is worrisome and is bound to cast a shadow on the inference estimates that may arise from such models. In this study, we have designed a novel application-based SCM for the first time using the background knowledge obtained from the Early Grade Reading Assessment (EGRA) program called the Strengthen Education in Northeast Nigeria (SENSE-EGRA), which is an educational intervention program of the American University of Nigeria (AUN), Yola, on the letter identification subtask. This project was sponsored by the United States Agency for International Development (USAID). We employed the conditional independence test (CIT) criteria for the validation of the SCM’s correctness, and the results show a near-perfect SCM.
Design and Validation of Structural Causal Model: A Focus on SENSE-EGRA Datasets Ayem, Gabriel Terna; Nsang , Augustine Shey; Igoche, Bernard Igoche; Naankang, Garba
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 1 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.1.200

Abstract

Designing and validating a causal model's correctness from a dataset whose background knowledge is obtained from a research process is not a common phenomenon. 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 designed a novel application-based SCM for the first time using the background knowledge gained from the American University of Nigeria (AUN), Yola, on the letter identification subtask of the Early Grade Reading Assessment (EGRA) program on the Strengthen Education in Northeast Nigeria (SENSE-EGRA) project dataset, which the USAID sponsored. We employed the conditional independence test (CIT) criteria for the model’s correctness validation testing, and the results show a near-perfect SCM.
Co-Authors Asilkan, Ozcan Igoche, Bernard Igoche Iorliam, Aamo Naankang, Garba Nsang , Augustine Shey Nsang, Augustine Shey