Causal inference for recurrent events via aggregated marginal odds ratio
Bibliographic record
Abstract
Researchers often work with treatments and outcomes that vary over time. For example, psychologists are interested in the curative effect of cognitive behavior therapies on patients' recurrent depression symptoms. While there are various causal effect measures designed for one-time treatment, the causal effect measures for time-varying treatment and recurrent events are relatively under-developed. In this article, a new causal measure is proposed to quantify the causal effect of time-varying treatments on recurrent events. We suggest estimators with robust standard errors that are based on various weight models for both conventional causal measures and the proposed measure in different time settings. We outline the approaches and describe how using some stabilized inverse probability weight models are more advantageous than others. We demonstrate that the proposed causal estimand can be consistently estimated for study periods of moderate length, and the estimation results are compared under different treatment settings with various weight models. We also find that the proposed method is suitable for both absorbing and nonabsorbing treatments. The methods are applied to the 1997 National Longitudinal Study of Youth as an illustrative example.
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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 | 0.033 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".