Goodness‐of‐fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The propensity score is defined to be a subject's probability of treatment selection, conditional on observed baseline covariates. Conditional on the propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. In the medical literature, there are three commonly employed propensity-score methods: stratification (subclassification) on the propensity score, matching on the propensity score, and covariate adjustment using the propensity score. Methods have been developed to assess the adequacy of the propensity score model in the context of stratification on the propensity score and propensity-score matching. However, no comparable methods have been developed for covariate adjustment using the propensity score. Inferences about treatment effect made using propensity-score methods are only valid if, conditional on the propensity score, treated and untreated subjects have similar distributions of baseline covariates. We develop both quantitative and qualitative methods to assess the balance in baseline covariates between treated and untreated subjects. The quantitative method employs the weighted conditional standardized difference. This is the conditional difference in the mean of a covariate between treated and untreated subjects, in units of the pooled standard deviation, integrated over the distribution of the propensity score. The qualitative method employs quantile regression models to determine whether, conditional on the propensity score, treated and untreated subjects have similar distributions of continuous covariates. We illustrate our methods using a large dataset of patients discharged from hospital with a diagnosis of a heart attack (acute myocardial infarction). The exposure was receipt of a prescription for a beta-blocker at hospital discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it