ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Modèle Structurel Marginal à Effet Traitement Hétérogène (HTE-MSM)×Pondération par score de propension (PSP / IPW)×
DomaineInférence causaleInférence causale
FamilleRegression modelRegression model
Année d'origine2000–2010s1983 (propensity score); 2003 (efficient IPW estimator)
Auteur d'origineRobins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010sRosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
TypeCausal inference / weighted regression with effect modificationCausal inference / reweighting
Source fondatriceRobins, 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 ↗
AliasHTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural modelPSW, inverse probability weighting, IPW, propensity-based weighting
Apparentées56
RésuméThe Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.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).
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Heterogeneous Treatment Effect Marginal Structural Model · Propensity Score Weighting. Consulté le 2026-06-19 sur https://scholargate.app/fr/compare