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| Randomizacja mendlowska× | Metoda regresji z nieciągłością (RDD)× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1997 | 2008 |
| Twórca≠ | George Davey Smith | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Typ≠ | Genetic instrumental variable framework | Quasi-experimental causal design |
| Źródło pierwotne≠ | Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89-R98. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Inne nazwy≠ | MR | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Pokrewne≠ | 2 | 5 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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