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Compară metode

Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.

Modelul Structural Marginal Spațial×Ponderarea Scorului de Propensitate (PSW / IPW)×
DomeniuInferență cauzalăInferență cauzală
FamilieRegression modelRegression model
Anul apariției2000s–2010s1983 (propensity score); 2003 (efficient IPW estimator)
Autorul originalRobins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literatureRosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
TipCausal inference / spatial weightingCausal inference / reweighting
Sursa seminalăRobins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗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. DOI ↗
Denumiri alternativeSpatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural modelPSW, inverse probability weighting, IPW, propensity-based weighting
Înrudite66
RezumatThe Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially varying exposures by constructing inverse probability weights that account for both individual covariates and spatial location, then fitting a weighted outcome model in the resulting pseudo-population.Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).
ScholarGateSet de date
  1. v1
  2. 2 Surse
  3. PUBLISHED
  1. v1
  2. 2 Surse
  3. PUBLISHED

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ScholarGateCompară metode: Spatial Marginal Structural Model · Propensity Score Weighting. Preluat la 2026-06-17 de pe https://scholargate.app/ro/compare