השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מ.ר. (Mendelian Randomization)× | תכנון רגרסיה בדידה (RDD)× | |
|---|---|---|
| תחום | הסקה סיבתית | הסקה סיבתית |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1997 | 2008 |
| הוגה השיטה≠ | George Davey Smith | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| סוג≠ | Genetic instrumental variable framework | Quasi-experimental causal design |
| מקור מכונן≠ | 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 ↗ |
| כינויים≠ | MR | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| קשורות≠ | 2 | 5 |
| תקציר≠ | 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. |
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