Sammenlign metoder

	Propensity Score Weighting (PSW / IPW)	Entropy Balancing
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	1983 (propensity score); 2003 (efficient IPW estimator)	2012
Ophavsperson	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Jens Hainmueller
Type	Causal inference / reweighting	Covariate-balancing reweighting
Oprindelig kilde	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.	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.
Aliasser	PSW, inverse probability weighting, IPW, propensity-based weighting	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Relaterede	6	6
Resumé	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).	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.