Vertaile menetelmiä

	Spatiaalinen kaksoisrobustinen estimointi	Kaksoisrobustin estimoinnin (AIPW) menetelmä
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2010s–2020s	2005
Kehittäjä	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Robins & Rotnitzky; Bang & Robins
Tyyppi	Semiparametric causal estimator	Semiparametric causal estimator
Alkuperäislähde	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.	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.
Rinnakkaisnimet	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Liittyvät	5	5
Tiivistelmä	Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.	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.