Design and Validation of Structural Causal Model: A Focus on EGRA Dataset - Gabriel Terna Ayem; Ozcan Asilkan; Aamo Iorliam (Universitas Dian Nuswantoro, 2023)
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.