Machine learning for causal inference: An application to ECLS-K data

Abstract

This paper explores the use of machine learning for causal inference to estimate the average treatment effect of special education services on fifth-grade math scores. Causal inference is the study of the relationship between cause and effect when changes in one variable directly affect another variable. The use of machine learning techniques in causal inference problems has been growing rapidly, offering advantages over traditional methods such as propensity score matching. such as propensity score matching. This paper compares the performance of four machine learning methods: Ordinary Least Squares (OLS), Multi-Layer Perception (MLP), Targeted Maximum Likelihood Estimation (TMLE), and Bayesian Additive Regression Trees (BART) in estimating the average treatment effect of special education services on fifth-grade math scores. This study utilizes the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) dataset. A factor analysis is conducted to identify the key variables that influence math performance, paving the way for examining their causal effects. Our results show that BART outperforms the other methods in accuracy and robustness and that receiving special education services does not have a causal effect on math scores. This paper discusses the implications and limitations of our findings and suggests directions for future study.