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Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Εύρωστη Στάθμιση Βαθμολογίας Προδιάθεσης× | Στάθμιση Βαθμολογίας Προδιάθεσης (PSW / IPW)× | |
|---|---|---|
| Πεδίο | Αιτιακή Συμπερασματολογία | Αιτιακή Συμπερασματολογία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1994–2019 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Δημιουργός≠ | Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Τύπος≠ | Robust causal weighting estimator | Causal inference / reweighting |
| Θεμελιώδης πηγή≠ | Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| Εναλλακτικές ονομασίες | robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
| ScholarGateΣύνολο δεδομένων ↗ |
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