Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| G-Computation (Fórmula G paramétrica)× | Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1986 | 2000 |
| Autor original≠ | James M. Robins | Robins, Hernán & Brumback |
| Tipo≠ | Parametric causal effect estimation | Causal inference weighting estimator |
| Fuente seminal≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias≠ | G-formula, Parametric G-formula, Standardization | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Relacionados≠ | 2 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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