Comparar métodos

	Regression Discontinuity in Sentencing	Desenho de Regressão por Descontinuidade (RDD)
Área	Criminology	Inferência causal
Família	Process / pipeline	Regression model
Ano de origem	1983	2008
Autor original	Richard A. Berk & David Rauma (criminological application); Donald L. Thistlethwaite & Donald T. Campbell (design origin)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tipo	Quasi-experimental causal design at a policy threshold	Quasi-experimental causal design
Fonte seminal	Berk, R. A., & Rauma, D. (1983). Capitalizing on nonrandom assignment to treatments: A regression-discontinuity evaluation of a crime-control program. Journal of the American Statistical Association, 78(381), 21–27.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Outros nomes	Sentencing Threshold RDD, Cutoff-Based Justice Evaluation, Risk-Score Discontinuity Design, Age-of-Majority Discontinuity	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Relacionados	4	5
Resumo	Regression discontinuity (RD) in sentencing exploits the sharp thresholds built into justice policy — sentencing-guideline cutoffs, the age of majority, risk-score thresholds that trigger detention or diversion — to estimate causal effects without a randomized trial. Units just above the cutoff receive a different treatment from units just below it, yet they are otherwise nearly identical, so comparing their outcomes isolates the effect of crossing the line. Berk and Rauma's 1983 evaluation of a crime-control program showed how criminologists can 'capitalize on nonrandom assignment' created by such rules.	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.