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Random Forest of Interaction Trees for Estimating Individualized Treatment Regimes with Ordered Treatment Levels in Observational Studies
Volume 21, Issue 2 (2023): Special Issue: Symposium Data Science and Statistics 2022, pp. 391–411
Justin Thorp   Richard A. Levine ORCID icon link to view author Richard A. Levine details   Luo Li     All authors (4)

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https://doi.org/10.6339/23-JDS1084
Pub. online: 2 February 2023      Type: Statistical Data Science      Open accessOpen Access

Received
31 July 2022
Accepted
6 January 2023
Published
2 February 2023

Abstract

Traditional methods for evaluating a potential treatment have focused on the average treatment effect. However, there exist situations where individuals can experience significantly heterogeneous responses to a treatment. In these situations, one needs to account for the differences among individuals when estimating the treatment effect. Li et al. (2022) proposed a method based on random forest of interaction trees (RFIT) for a binary or categorical treatment variable, while incorporating the propensity score in the construction of random forest. Motivated by the need to evaluate the effect of tutoring sessions at a Math and Stat Learning Center (MSLC), we extend their approach to an ordinal treatment variable. Our approach improves upon RFIT for multiple treatments by incorporating the ordered structure of the treatment variable into the tree growing process. To illustrate the effectiveness of our proposed method, we conduct simulation studies where the results show that our proposed method has a lower mean squared error and higher optimal treatment classification, and is able to identify the most important variables that impact the treatment effect. We then apply the proposed method to estimate how the number of visits to the MSLC impacts an individual student’s probability of passing an introductory statistics course. Our results show that every student is recommended to go to the MSLC at least once and some can drastically improve their chance of passing the course by going the optimal number of times suggested by our analysis.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Open access article under the CC BY license.

Keywords
educational data mining generalized propensity scores individualized treatment effect machine learning student success study

Funding
This research was supported in part by the National Science Foundation grant 1633130.

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