เปรียบเทียบวิธี

	Regression Discontinuity in Elections	Difference-in-Differences (DiD)
สาขาวิชา	Political Science	เศรษฐมิติ
ตระกูล	Process / pipeline	Regression model
ปีกำเนิด	2008	1994
ผู้ริเริ่ม	David S. Lee (electoral application); broader RD tradition	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
ประเภท	Quasi-experimental causal design using a vote-share threshold	Causal inference / panel regression
แหล่งต้นตำรับ	Lee, D. S. (2008). Randomized Experiments from Non-random Selection in U.S. House Elections. Journal of Econometrics, 142(2), 675–697.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
ชื่อเรียกอื่น	Close-election RD, Electoral regression discontinuity, Vote-share RD design, Incumbency-advantage RD	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.