Assessing the effectiveness of special education services on fifth grade math scores: Using traditional and machine learning methods with ECLS-K data

The Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) is a well-known research endeavor in the field of child development. In this research, some special education services are offered to those students who need supplementary support in some aspects. In this paper, our study aims to estimate the average treatment effect on students fifth grade math scores and assesses the effectiveness of these special education services based on the ECLS-K dataset, through both machine learning methods and traditional methods. We introduce Donald Rubins causal model and Propensity Score Analysis in the part of traditional methods, and Ordinary Least Squares (OLS), Targeted Maximum Likelihood Estimation (TMLE), Bayesian Additive Regression Trees (BART), Generalized Random Forests (GRF) and Double Machine Learning (DML) in the part of machine learning methods. Finally, we employ Propensity Score Matching, OLS and BART to estimate the ATE. All estimated ATEs are significantly different from zero. The estimated ATEs are found to be minus, suggesting that these special education services may have a negative effect on students fifth grade math scores. Obviously, this conclusion is inconsistent with the original intent of these services, which aimed to have a positive impact.