Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908117443280896 |
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| author | Nagai, Tomoko Okuda, Takayuki Nakamura, Tomoya Sato, Yuichiro Sato, Yusuke Kinjo, Kensaku Kawamura, Kengo Kikuta, Shin Kumano-go, Naoto |
| author_facet | Nagai, Tomoko Okuda, Takayuki Nakamura, Tomoya Sato, Yuichiro Sato, Yusuke Kinjo, Kensaku Kawamura, Kengo Kikuta, Shin Kumano-go, Naoto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments Nagai, Tomoko Okuda, Takayuki Nakamura, Tomoya Sato, Yuichiro Sato, Yusuke Kinjo, Kensaku Kawamura, Kengo Kikuta, Shin Kumano-go, Naoto Methodology Machine Learning Econometrics 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. |
| title | Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments |
| topic | Methodology Machine Learning Econometrics |
| url | https://arxiv.org/abs/2411.01498 |