ScholarGate
Assistant

Comparer des méthodes

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

Évaluation de politiques par pondération par l'inverse de la probabilité×Pondération par score de propension (PSP / IPW)×
DomaineInférence causaleInférence causale
FamilleRegression modelRegression model
Année d'origine1952 (IPW origin); 2000s (policy evaluation application)1983 (propensity score); 2003 (efficient IPW estimator)
Auteur d'origineHorvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
TypeReweighting estimator for causal policy analysisCausal inference / reweighting
Source fondatriceImbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. 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 ↗
AliasIPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weightingPSW, inverse probability weighting, IPW, propensity-based weighting
Apparentées66
RésuméPolicy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.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: Policy Evaluation Inverse Probability Weighting · Propensity Score Weighting. Consulté le 2026-06-19 sur https://scholargate.app/fr/compare