Salīdzināt metodes

	Telpiskā noslieces rādītāja svēršana	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000s–2010s	2000
Autors	Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs	Robins, Hernán & Brumback
Tips	Quasi-experimental / causal inference	Causal inference weighting estimator
Pirmavots	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	spatial PSW, geographically weighted propensity score weighting, spatial IPTW, spatially adjusted inverse probability weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	6	5
Kopsavilkums	Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data.	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.