Improved sequential decision-making with structural priors: Enhanced treatment personalization with historical data
Bibliographic record
Abstract
Personalizing treatments for patients involves a period where different treatments out of a set of available treatments are tried until an optimal treatment is found, for particular patient characteristics. To minimize suffering and other costs, it is critical to minimize this search. When treatments have primarily short-term effects, the search can be performed with multi-armed bandit algorithms (MABs). However, these typically require long exploration periods to guarantee optimality. With historical data, it is possible to recover a structure incorporating the prior knowledge of the types of patients that can be encountered, and the conditional reward models for those patient types. Such structural priors can be used to reduce the treatment exploration period for enhanced applicability in the real world. This thesis presents work on designing MAB algorithms that find optimal treatments quickly, by incorporating a structural prior for patient types in the form of a latent variable model. Theoretical guarantees for the algorithms, including a lower and a matching upper bound, and an empirical study is provided, showing that incorporating latent structural priors is beneficial. Another line of work in this thesis is the design of simulators for evaluating treatment policies and comparing algorithms. A new simulator for benchmarking estimators of causal effects, the Alzheimer’s Disease Causal estimation Benchmark (ADCB) is presented. ADCB combines data-driven simulation with subject-matter knowledge for high realism and causal verifiability. The design of the simulator is discussed, and to demonstrate its utility, the results of a usage scenario for evaluating estimators of causal effects are outlined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".