Sammenlign metoder

	G-beregning (parametrisk G-formel)	Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	1986	2000
Ophavsperson	James M. Robins	Robins, Hernán & Brumback
Type	Parametric causal effect estimation	Causal inference weighting estimator
Oprindelig kilde	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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliasser	G-formula, Parametric G-formula, Standardization	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relaterede	2	5
Resumé	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.