A comparison of propensity score-based causal estimators for analyzing partially missing confounder
Bibliographic record
Abstract
The propensity scores (PSs) have been developed by modelling the treatment allocation mechanism on observed covariates to recover the balance in observational studies.However, in addition to confounding, missing data often arise in observational studies, and the consequence will be severe if the confounder, a covariate that affects both treatment and outcome, is missing.This study developed an expected-maximization (EM) algorithm to estimate the PS hence the PS-based estimators with confounder missingness under missing at random assumption, and compares the estimator's performance with complete case analysis and multiple imputation approaches.The EM method is most efficient for the stratification and regression estimator, and the multiple imputation approach is efficient for matching and inverse probability weighting estimator.In the simulation, we compared the seconds per iteration to assess the computational burden for different methods of dealing with confounder missingness.The computational time for multiple imputation approaches is significantly higher than the EM and complete case analysis for a given missing percentage and the number of imputations.Therefore, the applied researchers may consider the EM algorithm to deal with missing data problems that provide instant but consistent results.Finally, an application to the breast cancer study and B-aware trial dataset are presented.
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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 | 0.049 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".