Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovské vyvažování entropie× | Vážení na základě skóre sklonu (PSW / IPW)× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2012-2020s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Tvůrce≠ | Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Typ≠ | Weighting-based causal estimator with Bayesian uncertainty quantification | Causal inference / reweighting |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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). |
| ScholarGateDatová sada ↗ |
|
|