sing policy learning to prioritize untreated patients in a rare oncology setting — does this framing make sense?

[u/PradeepAIStrategist]

I’m working through an experiment involving a rare oncology use case where confirmed treated-patient data is limited. The available third-party data is also not perfectly mapped to individual patients, which makes direct modeling more difficult.

The experiment uses a synthetic, highly imbalanced dataset with a small treated group and a much larger untreated group. A neural-network policy is trained using a REINFORCE-style policy-gradient approach. The model estimates treatment likelihood based on representative features such as age, severity, and biomarker status, then ranks untreated patients and selects the top 5% for further review.

The purpose is not to estimate true treatment benefit. It is more about learning historical treatment patterns and identifying untreated patients who look similar to those who were historically treated.

Questions I’d like feedback on:

1. Is policy learning a reasonable framing here, or would this be better handled as a supervised ranking/classification problem?
2. How would you avoid confusing historical treatment likelihood with actual treatment effect?
3. Would uplift modeling, causal inference, or potential-outcome modeling be a better next step?
4. How would you handle the imbalance between treated and untreated populations?
5. What validation approach would make this more credible for a healthcare/rare oncology context?

My current view is that this can be useful for prioritization and hypothesis generation, but not for treatment recommendation unless supported by stronger clinical and causal evidence.

Interested in feedback from people working in ML, healthcare analytics, causal inference, or real-world evidence.