Sammenlign metoder

	Vægtning med den inverse sandsynlighed i uddannelsesforskning	Instrumentalvariabel (IV) Metoden til Kausal Inferens
Fagområde	Kausal inferens	Sundhedsøkonomi
Familie	Regression model	Process / pipeline
Oprindelsesår	1983–2003	1990s (modern applications)
Ophavsperson	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal weighting estimator	Method
Oprindelig kilde	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Aliasser	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	IV, two-stage least squares, TSLS, causal estimation
Relaterede	6	3
Resumé	Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.