Random Forest for Individualized Treatment Regimes in Observational Student Success Studies

• Activity Number: 27 - SPEED: Statistical Learning and Data Challenge Part 1
• Type: Contributed
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Juanjuan Fan* and Luo Li and Richard A Levine
• Companies: San Diego State University and San Diego State University and San Diego State University
• Keywords: generalized propensity score; machine learning; educational data mining; supplemental instruction; random forest of interaction trees

Abstract

Individuals may respond to treatments with significant heterogeneity. To optimize the treatment effect, it is necessary to recommend treatments based on individual characteristics. Existing methods in the literature for learning individualized treatment regimes are usually designed for randomized studies with binary treatments. In this study, we propose an algorithm to extend random forest of interaction trees to accommodate multiple treatments. By integrating the generalized propensity score into the interaction tree growing process, the proposed method can handle observational study data with multiple treatments. The performance of the proposed method, relative to existing approaches in the literature, is evaluated through simulation studies. The proposed method is applied to an assessment of multiple voluntary educational programs at a large public university.