Comparar métodos

	Algoritmos de Descoberta Causal (PC, FCI, LiNGAM)	Desenho de Regressão por Descontinuidade (RDD)	Variáveis Instrumentais via Mínimos Quadrados em Dois Estágios (IV/2SLS)
Área	Inferência causal	Inferência causal	Inferência causal
Família	Regression model	Regression model	Regression model
Ano de origem	2000	2008	2009
Autor original	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Tipo	Causal structure learning	Quasi-experimental causal design	Instrumental-variables regression
Fonte seminal	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomes	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	RDD, regression discontinuity design, sharp RDD, fuzzy RDD	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Relacionados	5	5	5
Resumo	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.	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.	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).