Vertaile menetelmiä

	Regressioepäjatkuvuussuunnittelu (RDD)	OLS-regressio (Ordinary Least Squares)
Tieteenala	Kausaalipäättely	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2008	2019
Kehittäjä	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Quasi-experimental causal design	Linear regression
Alkuperäislähde	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	RDD, regression discontinuity design, sharp RDD, fuzzy RDD	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	5	5
Tiivistelmä	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).