Salīdzināt metodes

	Politikas novērtēšana ar tendenču rādītāju svēršanu	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1983/2003	2000
Autors	Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)	Robins, Hernán & Brumback
Tips	Quasi-experimental causal inference	Causal inference weighting estimator
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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	6	5
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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.