Confronta i metodi

	Ponderazione Robusta del Punteggio di Propensione	Stima a Doppia Robustezza (AIPW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	1994–2019	2005
Ideatore	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Robins & Rotnitzky; Bang & Robins
Tipo	Robust causal weighting estimator	Semiparametric causal estimator
Fonte seminale	Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.	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.
Alias	robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Correlati	6	5
Sintesi	Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.	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.