เปรียบเทียบวิธี

	Regression Discontinuity in Elections	การออกแบบการถดถอยแบบไม่ต่อเนื่อง (Regression Discontinuity Design - RDD)
สาขาวิชา	Political Science	การอนุมานเชิงสาเหตุ
ตระกูล	Process / pipeline	Regression model
ปีกำเนิด	2008	2008
ผู้ริเริ่ม	David S. Lee (electoral application); broader RD tradition	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
ประเภท	Quasi-experimental causal design using a vote-share threshold	Quasi-experimental causal design
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Close-election RD, Electoral regression discontinuity, Vote-share RD design, Incumbency-advantage RD	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
ที่เกี่ยวข้อง	3	5
สรุป	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.