方法对比

	G-计算（参数G-公式）	双重稳健估计（AIPW）	逆概率治疗加权法 (IPW / IPTW)
领域	因果推断	因果推断	因果推断
方法族	Regression model	Regression model	Regression model
起源年份	1986	2005	2000
提出者	James M. Robins	Robins & Rotnitzky; Bang & Robins	Robins, Hernán & Brumback
类型	Parametric causal effect estimation	Semiparametric causal estimator	Causal inference weighting estimator
开创性文献	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. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
别名	G-formula, Parametric G-formula, Standardization	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
相关	2	5	5
摘要	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.	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.