Assessing the Causal Effect of Special Education Services on Math Achievement: A Causal Inference and Machine Learning Study

Abstract

This study aims to assess the Average Treatment Effect (ATE) of receiving special education services on revised Item Response Theory (IRT) scaled math achievement test scores. By employing a methodological repertoire comprising linear regression with ordinary least squares (OLS), propensity score matching (PSM), Bayesian Additive Regression Trees (BART), and Multilayer Perceptron (MLP), we examine the impact of these interventions. Leveraging data from the Early Childhood Longitudinal Study Kindergarten 2010-11 cohort (ECLS-K:2011), we systematically analyze the ATE of special education services on students' math achievement. The results show that all models yield negative ATE results, suggesting a deleterious effect of special education services on fifth-grade math scores. Furthermore, we employ Principal Component Analysis (PCA) to corroborate these findings, aligning with outcomes obtained from causal inference and Machine Learning (ML) based methods. This research emphasizes the importance of method diversity in educational research and highlights the need for assessments of intervention effectiveness to help educational practices and policies.