A case study of estimating a causal effect using machine learning with Bayesian Additive Regression Trees

Abstract

The study aims to introduce a causal inference method using machine learning to general education researchers, and in particular, focus on the theory and practice of Bayesian Additive Regression Trees algorithm. To analyze the empirical data, public data from the Korean Children and Youth Panel Survey 2018 were used. For an illustrative purpose, this study estimated the causal effect of participation in activities related to self (personality) development on students’ life satisfaction and self-esteem and discussed the feasibility of the BART method in educational impact studies. The applicability of the BART-based machine learning causal inference technique in the field of education was discussed in comparison with model-based propensity score and causal effect estimation. Finally future research topics and limitations of the study were addressed.