Design and Validation of Structural Causal Model: A Focus on SENSE-EGRA Datasets
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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.
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