Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Marginaal Structureel Model voor Beleidsevaluatie× | Dubbel Robuuste Schatting (AIPW)× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2000 | 2005 |
| Grondlegger≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins & Rotnitzky; Bang & Robins |
| Type≠ | Causal inference / weighted regression | Semiparametric causal estimator |
| Oorspronkelijke bron≠ | Robins, J. M., Hernan, 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 ↗ |
| Aliassen | MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data. | 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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