| Owner: |
Chenguang Pan
|
| Owner Email: |
grepan3280@gmail.com
|
| Paper Title: |
Designing Optimal Dynamic Treatment Regimes Using TMLE for Personalized Math Course-Taking Plans
|
| Session Title: |
Machine Learning Techniques I: Advancing Equity, Prediction, and Measurement (Table 4)
|
| Paper Type: |
Roundtable Presentation
|
| Presentation Date: |
4/25/2025
|
| Presentation Location: |
Denver, CO
|
| Descriptors: |
Data-driven decision making, High Schools, Research Methodology
|
| Methodology: |
Quantitative
|
| Author(s): |
Chenguang Pan, Teachers College, Columbia University; Youmi Suk, Teachers College, Columbia University
|
| Unit: |
Division D - Measurement and Research Methodologies
|
| Abstract: |
This study provides an approach to designing optimal dynamic treatment regimes (DTRs) using Targeted Maximum Likelihood Estimation (TMLE), coupled with ensemble learning algorithms, to build a personalized recommendation model for high school math course-taking plans. Our method uses backward induction and feasibility constraints to create personalized, data-driven recommendations under practical considerations. Our simulation study demonstrates that the proposed DTR-TMLE method yields more accurate recommendations compared to Q-learning based on linear regression. We apply the TMLE method to design math course recommendations using data from the High School Longitudinal Study of 2009 (HSLS:09), ultimately aiming to recommend the right math course for each student at the right time.
|
| DOI: |
https://doi.org/10.3102/2190733
|