Сравнение методов

	Regression Discontinuity in Elections	Регрессионный разрывный дизайн (RDD)
Область	Political Science	Причинно-следственный вывод
Семейство	Process / pipeline	Regression model
Год появления	2008	2008
Автор метода	David S. Lee (electoral application); broader RD tradition	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Тип	Quasi-experimental causal design using a vote-share threshold	Quasi-experimental causal design
Основополагающий источник	Lee, D. S. (2008). Randomized Experiments from Non-random Selection in U.S. House Elections. Journal of Econometrics, 142(2), 675–697.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Другие названия	Close-election RD, Electoral regression discontinuity, Vote-share RD design, Incumbency-advantage RD	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Связанные	3	5
Сводка	Regression discontinuity in elections is a quasi-experimental design that exploits the sharp winning threshold in electoral contests to estimate causal effects of holding office. Just above the threshold a candidate or party wins; just below, it loses. In very close races, which side ends up just over the line is plausibly as good as random, so comparing the later outcomes of bare winners and bare losers identifies the causal effect of winning — most famously the incumbency advantage — without confounding by candidate or district quality.	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.