Sammenlign metoder

	Dobbelt robust estimering inden for uddannelsesforskning	Propensity Score Matching
Fagområde	Kausal inferens	Forskningsstatistik
Familie	Regression model	Process / pipeline
Oprindelsesår	1994-2005	1983
Ophavsperson	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Paul Rosenbaum and Donald Rubin
Type	Causal inference / semiparametric estimator	Method
Oprindelig kilde	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	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.
Aliasser	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	PSM, propensity score weighting, covariate balance
Relaterede	6	3
Resumé	Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-random but observed covariates can account for selection bias. The estimator is consistent if either — not necessarily both — of the two component models is correctly specified.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.