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

Author

Listed:
  • Tomoko Nagai
  • Takayuki Okuda
  • Tomoya Nakamura
  • Yuichiro Sato
  • Yusuke Sato
  • Kensaku Kinjo
  • Kengo Kawamura
  • Shin Kikuta
  • Naoto Kumano-go

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.

Suggested Citation

  • Tomoko Nagai & Takayuki Okuda & Tomoya Nakamura & Yuichiro Sato & Yusuke Sato & Kensaku Kinjo & Kengo Kawamura & Shin Kikuta & Naoto Kumano-go, 2024. "Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments," Papers 2411.01498, arXiv.org.
    • Handle: RePEc:arx:papers:2411.01498
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    References listed on IDEAS

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    1. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    2. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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