Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Potrivirea scorului de propensitate pentru evaluarea politicilor× | Ponderarea Scorului de Propensitate (PSW / IPW)× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1983; policy evaluation adaptation 1997 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Autorul original≠ | Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Tip≠ | Quasi-experimental matching estimator | Causal inference / reweighting |
| Sursa seminală | 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 ↗ |
| Denumiri alternative | PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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 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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