Sammenlign metoder

	Sensitivitetsanalyse for kausalitet i utdanningsforskning	Regresjonsdiskontinuitetsdesign (RDD)
Fagfelt	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Opprinnelsesår	1983–2002	2008
Opphavsperson	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Type	Causal robustness / bias assessment	Quasi-experimental causal design
Opprinnelig kilde	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Alias	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Relaterte	6	5
Sammendrag	Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.