TITLE:
Estimating Average and Heterogeneous Effects of Special Education: A Causal Machine Learning Perspective
AUTHORS:
Siyi Ruan
KEYWORDS:
Special Education, Causal Inference, Average Treatment Effect, Propensity Score Matching, Causal Machine Learning
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.15 No.4,
October
27,
2025
ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the causal effect of special education services on fifth-grade mathematics performance using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K). To address confounding, we employ both traditional and modern causal inference methods. First, propensity score matching (PSM) is implemented to create balanced treatment and control groups. Complementarily, three machine learning-based approaches—Bayesian Additive Regression Trees (BART), Causal Forests (CF), and Double Machine Learning (DML)—are applied to flexibly capture nonlinear relationships and treatment heterogeneity. Across all methods, we find consistently negative average treatment effects, with PSM and DML estimating stronger impacts, while BART and CF reveal moderately negative but significant effects. Importantly, machine learning models uncover substantial individual variation, indicating that while many students experience adverse outcomes, some benefit from targeted interventions. Variable importance analysis highlights early academic ability, socioeconomic background, and parental expectations as key predictors of later achievement, suggesting that observed disadvantages largely predate special education assignment. These findings underscore the methodological value of combining traditional and machine learning approaches for causal inference and carry important policy implications for designing equitable, preventive, and individualized educational interventions.