Confronta i metodi

	Randomizzazione Mendeliana	Disegno a Regressione Discontinua (RDD)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	1997	2008
Ideatore	George Davey Smith	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tipo	Genetic instrumental variable framework	Quasi-experimental causal design
Fonte seminale	Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89-R98.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Alias	MR	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Correlati	2	5
Sintesi	Mendelian randomization is a method for estimating causal effects of exposures on outcomes using genetic variants as instrumental variables. Introduced by George Davey Smith in the 1990s, it exploits Mendel's law of segregation to remove confounding bias. It has become a cornerstone technique in epidemiological causal inference.	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.