Sammenlign metoder

	Regresjonsdiskontinuitetsdesign for politikkevaluering	Instrumentelle variabler (IV) metode for kausal inferens
Fagfelt	Kausal inferens	Helseøkonomi
Familie	Regression model	Process / pipeline
Opprinnelsesår	1960; policy evaluation applications widespread from 2000s	1990s (modern applications)
Opphavsperson	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Quasi-experimental causal design	Method
Opprinnelig kilde	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact	IV, two-stage least squares, TSLS, causal estimation
Relaterte	5	3
Sammendrag	Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.	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.