Causal Machine Learning for Special Education: Estimating Heterogeneous Effects on Elementary Math Achievement

Despite extensive special education investments, their causal impact on elementary mathematics remains debated due to confounding factors in observational data. This study leverages causal machine learning models, including Bayesian Additive Regression Trees (BART) and Causal Forests, to estimate heterogeneous effects of special education on elementary mathematics achievement. Using longitudinal data from 7,362 U.S. students (ECLS-K:2011), we implement a four-stage pipeline: (1) LASSO-PLS preprocessing for covariate selection and dimension reduction; (2) Propensity Score Matching (PSM) to address selection bias; (3) BART for Bayesian treatment effect estimation; and (4) Causal Forests for subgroup analysis. Results show no significant average treatment effect (ATE = -0.69, p=0.707) after matching, but reveal critical heterogeneity: students with mid-range kindergarten math ability (MIRT 50-70) gain 6-8 points, while public schools buffer negative effects for low-ability learners. Family background factors show no moderation effect. These findings demonstrate that special education's efficacy depends fundamentally on academic readiness, supporting precision resource allocation in educational policy.