Comparar métodos

	Regression discontinuity design in education research	Método de Variáveis Instrumentais (VI) para Inferência Causal
Área	Inferência causal	Economia da saúde
Família	Regression model	Process / pipeline
Ano de origem	1960 (origination); 1999-2010 (education economics canon)	1990s (modern applications)
Autor original	Thistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Quasi-experimental causal inference	Method
Fonte seminal	Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281-355.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Outros nomes	RDD in education, education RD design, sharp RDD education, score-cutoff design	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumo	Regression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on student or school outcomes. Units just below and just above the cutoff are treated as near-randomly assigned, enabling credible causal inference without a randomized trial.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.