Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
This addresses the problem of accurately measuring educational interventions for university students, but it is incremental as it extends an existing method to a specific context.
The study tackled the underestimation of an Academic Support Center's impact due to group bias by applying causal inference to evaluate the conditional average treatment effect (CATE) of face-to-face assistance, predicting its performance based on the number of sessions.
This study examines the educational effect of the Academic Support Center at Kogakuin University. Following the initial assessment, it was suggested that group bias had led to an underestimation of the Center's true impact. To address this issue, the authors applied the theory of causal inference. By using T-learner, the conditional average treatment effect (CATE) of the Center's face-to-face (F2F) personal assistance program was evaluated. Extending T-learner, the authors produced a new CATE function that depends on the number of treatments (F2F sessions) and used the estimated function to predict the CATE performance of F2F assistance.