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

Tomoko Nagai, Takayuki Okuda, Tomoya Nakamura, Yuichiro Sato, Yusuke Sato +4 more
11/3/2024
stat.MEcs.LGecon.EM

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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.

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Cite this paper

@article{nagai2024educational,
  title  = {Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments},
  author = {Tomoko Nagai and Takayuki Okuda and Tomoya Nakamura and Yuichiro Sato and Yusuke Sato and Kensaku Kinjo and Kengo Kawamura and Shin Kikuta and Naoto Kumano-go},
  year   = {2024},
  eprint = {2411.01498},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.01498v1}
}

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