Learning Feasible Optimal Treatment Regimes for Personalized Decision-Making

Personalized decision-making is essential in psychology and education for addressing the specific needs, interests, and abilities of individuals. A popular, data-driven approach for personalized recommendations is based on optimal treatment regimes (OTRs), which finds a decision rule that maximizes a desirable mean outcome by leveraging treatment effect heterogeneity. However, standard OTRs may yield infeasible recommendations when institutional, developmental, or contextual factors restrict available options. This study introduces a general framework for feasible OTRs that explicitly integrates feasibility constraints into individualized decision rules in two ways: (a) a priori rules informed by domain knowledge (e.g., prerequisites) and (b) propensity-score-based trimming that excludes rarely observed treatment options. We conduct a series of simulation studies to systematically evaluate the performance of feasible OTR methods and compare Targeted Maximum Likelihood Estimation (TMLE) with Q-learning. Our first simulation study shows that the TMLE method performs comparably to an oracle Q-learning model when feasibility constraints are applied. The second simulation study identifies consistent ranges of optimal thresholds (0.01–0.08) across scenarios with varying numbers of treatment levels. We also develop a semi-synthetic calibration procedure to determine dataset-specific thresholds. Using data from the High School Longitudinal Study of 2009 (HSLS:09), we demonstrate the proposed framework by developing a personalized math course-taking recommendation model. The resulting feasible OTRs recommend personalized yet realistic course placements and increase the expected ninth-grade GPA relative to observed assignments. These results provide systematic evidence on incorporating feasibility into OTRs and offer practical guidance for designing realistic OTRs in personalized decision-making.