Bibliographic record
Abstract
This paper proposes strategies for defining, identifying, and estimating features of treatmenteffect distributions in contexts where multiple outcomes are of interest.After describing existing empirical approaches used in such settings, the paper develops a notion of treatment preference that is shown to be a feature of standard treatment-effect analysis in the single-outcome case.Focusing largely on binary outcomes, treatment-preference probability treatment effects (PTEs) are defined and are seen to correspond to familiar average treatment effects in the single-outcome case.The paper suggests seven possible characterizations of treatment preference appropriate to multiple-outcome contexts.Under standard assumptions about unconfoundedness of treatment assignment, the PTEs are shown to be point identified for three of the seven characterizations and set identified for the other four.Probability bounds are derived and empirical approaches to estimating the bounds-or the PTEs themselves in the point-identified cases-are suggested.These empirical approaches are straightforward, involving in most instances little more than estimation of binary-outcome probability models of what are commonly known as composite outcomes.The results are illustrated with simulated data and in analyses of two microdata samples.Finally, the main results are extended to situations where the component outcomes are ordered or categorical.
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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 | 0.053 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".