Sammenlign metoder

	Romlig dobbelt robust estimering	Propensity Score Matching
Fagfelt	Kausal inferens	Forskningsstatistikk
Familie	Regression model	Process / pipeline
Opprinnelsesår	2010s–2020s	1983
Opphavsperson	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Paul Rosenbaum and Donald Rubin
Type	Semiparametric causal estimator	Method
Opprinnelig kilde	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	PSM, propensity score weighting, covariate balance
Relaterte	5	3
Sammendrag	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.