Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| G-arvutamine (Parametriline G-valem)× | Topeltrobustne hindamine (AIPW)× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1986 | 2005 |
| Looja≠ | James M. Robins | Robins & Rotnitzky; Bang & Robins |
| Tüüp≠ | Parametric causal effect estimation | Semiparametric causal estimator |
| Algallikas≠ | Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512. 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 ↗ |
| Rööpnimetused≠ | G-formula, Parametric G-formula, Standardization | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Seotud≠ | 2 | 5 |
| Kokkuvõte≠ | G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models. | 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. |
| ScholarGateAndmestik ↗ |
|
|