Bibliographic record
Abstract
Abstract In causal inference, semiparametric estimation using propensity scores has rapidly developed in various directions. At the same time, although model selection is indispensable in statistical analysis, an information criterion for selecting the regression structure between the potential outcome and explanatory variables has not been well developed. Here, based on the original definition of AIC, we derive an AIC‐type criterion for propensity score analysis. A risk based on the Kullback–Leibler divergence is defined as the cornerstone, and general causal inference models and general causal effects are treated. Considering the high importance of doubly robust estimation, we make the information criterion itself doubly robust so that it is an asymptotically unbiased estimator of the risk even under some model misspecification. In simulation studies, we compare the derived criterion with an existing weighted quasi‐likelihood information criterion and confirm that the former outperforms the latter. Real data analyses indicate that results using the two criteria can differ significantly.
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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 | 0.000 | 0.003 |
| 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".