ScholarGate
Assistente

Comparar métodos

Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Propensity Score Matching para Avaliação de Políticas×Ponderação por Escore de Propensão (PEP / IPW)×
ÁreaInferência causalInferência causal
FamíliaRegression modelRegression model
Ano de origem1983; policy evaluation adaptation 19971983 (propensity score); 2003 (efficient IPW estimator)
Autor originalRosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation applicationRosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
TipoQuasi-experimental matching estimatorCausal inference / reweighting
Fonte seminalRosenbaum, 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 ↗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 ↗
Outros nomesPSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluationPSW, inverse probability weighting, IPW, propensity-based weighting
Relacionados66
ResumoPolicy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.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).
ScholarGateConjunto de dados
  1. v1
  2. 2 Fontes
  3. PUBLISHED
  1. v1
  2. 2 Fontes
  3. PUBLISHED

Ir para a pesquisa Baixar slides

ScholarGateComparar métodos: Policy Evaluation Propensity Score Matching · Propensity Score Weighting. Recuperado em 2026-06-19 de https://scholargate.app/pt/compare