Linganisha mbinu

	Uthabiti wa Kina wa Angani (Spatial Doubly Robust Estimation)	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010s–2020s	2000
Mwanzilishi	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Robins, Hernán & Brumback
Aina	Semiparametric causal estimator	Causal inference weighting estimator
Chanzo asilia	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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Majina mbadala	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Zinazohusiana	5	5
Muhtasari	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.	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.