TITLE:
Services on Academic Achievement: A Robust Estimation Using Bayesian Additive Regression Trees
AUTHORS:
Yuntian Zuo
KEYWORDS:
Bayesian Additive Regression Trees, Targeted Maximum Likelihood Estimation, Unmeasured Confounding, Heterogeneous Treatment Effects, Special Education Causal Impact
JOURNAL NAME:
Open Journal of Statistics,
Vol.15 No.6,
November
27,
2025
ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is facing challenges from persistent selection bias and complex confounding. Bayesian Additive Regression Trees (BART) is employed in this study to provide a flexible estimation of the academic performance. Targeted Maximum Likelihood Estimation (TMLE) is also integrated into the BART model, supporting doubly robust estimation of the special education effect. This study extracted survey data from the Early Childhood Longitudinal Study, Kindergarten Class (ECLS-K), to estimate the causal impact of special education status on students’ combined mathematics and reading achievement scores. The analysis results of the BART-TMLE model show that children receiving special education services demonstrated approximately 9 points lower scores on average for combined math and reading scores, even adjusting for a considerable number of covariates, compared to their peers who did not receive these services. The estimated negative treatment effect persists after controlling for observed covariates that are closely correlated to the combined test score. The negative effect likely reflects unobserved factors, such as the underlying severity of learning disabilities, parent involvement and other potential traits, which are actual factors that determine the placement of special education status, rather than indicating the ineffectiveness of special education service. The achievement gap in academic performance reflects the current observable status of special education. The estimated effect could be improved by future research incorporating educational domain knowledge, allowing the model to be constructed more accurately.