Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Evaluación de políticas mediante emparejamiento por puntuación de propensión× | Emparejamiento por Puntuación de Propensión× | |
|---|---|---|
| Campo≠ | Inferencia causal | Estadística para la investigación |
| Familia≠ | Regression model | Process / pipeline |
| Año de origen≠ | 1983; policy evaluation adaptation 1997 | 1983 |
| Autor original≠ | Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application | Paul Rosenbaum and Donald Rubin |
| Tipo≠ | Quasi-experimental matching estimator | Method |
| Fuente 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 ↗ |
| Alias≠ | PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation | PSM, propensity score weighting, covariate balance |
| Relacionados≠ | 6 | 3 |
| Resumen≠ | Policy 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 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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