השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| משקולות הסתברות הפוכות (IPW / IPTW)× | אמידה חסונה כפולה (AIPW)× | |
|---|---|---|
| תחום | הסקה סיבתית | הסקה סיבתית |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2000 | 2005 |
| הוגה השיטה≠ | Robins, Hernán & Brumback | Robins & Rotnitzky; Bang & Robins |
| סוג≠ | Causal inference weighting estimator | Semiparametric causal estimator |
| מקור מכונן≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. 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 ↗ |
| כינויים≠ | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| קשורות | 5 | 5 |
| תקציר≠ | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. | 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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