Near-Equivalent Q-learning Policies for Dynamic Treatment Regimes
Precision medicine aims to tailor therapeutic decisions to individual patient characteristics. This objective is commonly formalized through dynamic treatment regimes, which use statistical and machine learning methods to derive sequential decision rules adapted to evolving clinical information. In most existing formulations, these approaches produce a single optimal treatment at each stage, leading to a unique decision sequence. However, in many clinical settings, several treatment options may yield similar expected outcomes, and focusing on a single optimal policy may conceal meaningful alternatives. We extend the Q-learning framework for retrospective data by introducing a worst-value ...
| Authors | Sophia Yazzourh, Erica E. M. Moodie |
|---|---|
| Primary category | stat.ML |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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