Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Propensity Score Matching em Pesquisa Educacional× | Ponderação por Escore de Propensão (PEP / IPW)× | |
|---|---|---|
| Área | Inferência causal | Inferência causal |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1983 (foundational); education adoption widespread from late 1990s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Autor original≠ | Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Tipo≠ | Quasi-experimental / matching-based causal inference | Causal inference / reweighting |
| Fonte seminal | 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 ↗ | 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 nomes | PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible. | 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). |
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