Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Estimador de Pareamento× | Propensity Score Matching× | |
|---|---|---|
| Área≠ | Inferência causal | Estatística para pesquisa |
| Família≠ | Regression model | Process / pipeline |
| Ano de origem≠ | 1973 | 1983 |
| Autor original≠ | Rubin (1973); large-sample theory by Abadie & Imbens (2006) | Paul Rosenbaum and Donald Rubin |
| Tipo≠ | Nonparametric matching / causal inference | Method |
| Fonte seminal≠ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. 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≠ | nearest-neighbor matching, NNM, matching on covariates, covariate matching | PSM, propensity score weighting, covariate balance |
| Relacionados≠ | 6 | 3 |
| Resumo≠ | The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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