Methoden vergleichen

	Fuzzy Regression Discontinuity Design in der Bildungsforschung	Propensity Score Matching
Fachgebiet	Kausale Inferenz	Forschungsstatistik
Familie	Regression model	Process / pipeline
Entstehungsjahr	Late 1990s–2000s	1983
Urheber	Imbens & Lemieux (2008); applied in education by Jacob & Lefgren (2004) and Angrist & Lavy (1999)	Paul Rosenbaum and Donald Rubin
Typ	Quasi-experimental / causal inference	Method
Wegweisende Quelle	Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.	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.
Aliasnamen	Fuzzy RDD, Fuzzy RD, Imperfect RDD, Non-sharp RD	PSM, propensity score weighting, covariate balance
Verwandt	4	3
Zusammenfassung	Fuzzy Regression Discontinuity Design (Fuzzy RDD) is a quasi-experimental causal method that exploits a known score threshold — such as a test cutoff — to estimate the effect of a program or intervention when assignment is imperfect. Widely used in education research to evaluate summer school, remedial programs, scholarships, and class-size rules, it uses two-stage least squares to recover a local average treatment effect for students near the threshold.	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.