Model Assessment in Dynamic Treatment Regimen Estimation via Double Robustness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamic treatment regimens (DTRs) recommend treatments based on evolving subject-level data. The optimal DTR is that which maximizes expected patient outcome and as such its identification is of primary interest in the personalized medicine setting. When analyzing data from observational studies using semi-parametric approaches, there are two primary components which can be modeled: the expected level of treatment and the expected outcome for a patient given their other covariates. In an effort to offer greater flexibility, the so-called doubly robust methods have been developed which offer consistent parameter estimators as long as at least one of these two models is correctly specified. However, in practice it can be difficult to be confident if this is the case. Using G-estimation as our example method, we demonstrate how the property of double robustness itself can be used to provide evidence that a specified model is or is not correct. This approach is illustrated through simulation studies as well as data from the Multicenter AIDS Cohort Study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it