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| Penimbang Kebolehpercayaan Songsang Kebarangkalian (Robust IPW)× | Anggaran Keboleh-Teguhan Berganda (AIPW)× | |
|---|---|---|
| Bidang | Inferens Kausal | Inferens Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2000-2004 | 2005 |
| Pengasas≠ | Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000) | Robins & Rotnitzky; Bang & Robins |
| Jenis≠ | Causal weighting estimator | Semiparametric causal estimator |
| Sumber perintis≠ | Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960. DOI ↗ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| Alias | Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
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