Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Echilibrarea Entropică Bayesiană× | Ponderarea Scorului de Propensitate (PSW / IPW)× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2012-2020s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Autorul original≠ | Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Tip≠ | Weighting-based causal estimator with Bayesian uncertainty quantification | Causal inference / reweighting |
| Sursa seminală≠ | Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. 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 | BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Înrudite | 6 | 6 |
| Rezumat≠ | Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals. | 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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