Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments

TL;DR

This paper evaluates the educational impact of a university support program using causal inference, specifically extending the T-learner method to model how treatment effects vary with the number of face-to-face sessions.

Contribution

It introduces a novel CATE function that depends on treatment quantity, enhancing understanding of how treatment effects vary with the number of sessions.

Findings

The proposed CATE function effectively captures treatment effect heterogeneity.

Application of causal inference reveals underestimation in previous assessments.

Extended T-learner improves prediction of treatment effects based on session count.

Abstract

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.