Balancing scores and causal diagrams
Bibliographic record
Abstract
BACKGROUND: The propensity score (PS) is the probability of exposure given the confounders and the disease risk score (DRS) is the probability of disease given confounders and the exposure, setting the exposure to a fixed value. PS and DRS are balancing scores and frequently used for confounding adjustment, especially in the presence of high-dimensional confounding. METHODS: Here, we use causal diagrams to present the role of PS and DRS in confounding adjustment. RESULTS: We graphically show the balancing properties of theoretical PS and DRS: conditional on PS, the exposure is independent of the confounders, and conditional on DRS, the potential outcome under no exposure is independent of the confounders. Moreover, we illustrate how PS and DRS can be used in the analysis for confounding adjustment. CONCLUSION: Causal diagrams can help researchers to better understand confounding adjustment by using PS and DRS.
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How this classification was reachedexpand
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".