Salīdzināt metodes

	Novērtēšanas vienādošana (Matching Estimator)	Aproksimēta novērtēšana (PSW / IPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1973	1983 (propensity score); 2003 (efficient IPW estimator)
Autors	Rubin (1973); large-sample theory by Abadie & Imbens (2006)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tips	Nonparametric matching / causal inference	Causal inference / reweighting
Pirmavots	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	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	nearest-neighbor matching, NNM, matching on covariates, covariate matching	PSW, inverse probability weighting, IPW, propensity-based weighting
Saistītās	6	6
Kopsavilkums	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.	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).