Inference and Causal Machine Learning in Educational Data Science: A Literature Review

Abstract

Educational Data Science seeks to gain knowledge and predict future events based on educational data. However, many analyses are still limited to description and prediction, without identifying the underlying causes. This article integrates two literature reviews: a narrative review of classical causal inference methods (Propensity Score Matching, Discontinuous Regression, Difference-in-Differences, and Synthetic Control) and a review of reviews on Causal Machine Learning (CML) techniques, such as Outcome Transformation, Meta-learners, and Causal Tree and Causal Forest algorithms. The analysis highlights the potential of these methodologies to assess the real impact of pedagogical interventions and personalize distance learning and e-learning, emphasizing the need for greater technical training and causal literacy among researchers and analysts.