Salīdzināt metodes

	Politikas novērtēšana ar tendenču rādītāju svēršanu	Aproksimēta novērtēšana (PSW / IPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1983/2003	1983 (propensity score); 2003 (efficient IPW estimator)
Autors	Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tips	Quasi-experimental causal inference	Causal inference / reweighting
Pirmavots	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	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.
Citi nosaukumi	PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW	PSW, inverse probability weighting, IPW, propensity-based weighting
Saistītās	6	6
Kopsavilkums	Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.	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).