Differences in target estimands between different propensity score‐based weights
Bibliographic record
Abstract
PURPOSE: Propensity score weighting is a popular approach for estimating treatment effects using observational data. Different sets of propensity score-based weights have been proposed, including inverse probability of treatment weights whose target estimand is the average treatment effect, weights whose target estimand is the average treatment effect in the treated (ATT), and, more recently, matching weights, overlap weights, and entropy weights. These latter three sets of weights focus on estimating the effect of treatment in those subjects for whom there is clinical equipoise. We conducted a series of simulations to explore differences in the value of the target estimands for these five sets of weights when the difference in means is the measure of treatment effect. METHODS: We considered 648 scenarios defined by different values of the prevalence of treatment, the c-statistic of the propensity score model, the correlation between the linear predictors for treatment selection and the outcome, and by the magnitude of the interaction between treatment status and the linear predictor for the outcome in the absence of treatment. RESULTS: We found that, when the prevalence of treatment was low or high and the c-statistic of the propensity score model was moderate to high, that matching weights, overlap weights, and entropy weights had target estimands that differed meaningfully from the target estimand of the ATE weights. CONCLUSIONS: Researchers using matching weights, overlap weights, and entropy weights should not assume that the estimated treatment effect is comparable to the ATE.
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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 | 0.100 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".